
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("/Users/prose/OneDrive/Desktop/Data/data.csv")
df.head()


#checking the column names
df.columns

Index(['Unnamed: 0', 'ID', 'Name', 'Age', 'Photo', 'Nationality', 'Flag',
       'Overall', 'Potential', 'Club', 'Club Logo', 'Value', 'Wage', 'Special',
       'Preferred Foot', 'International Reputation', 'Weak Foot',
       'Skill Moves', 'Work Rate', 'Body Type', 'Real Face', 'Position',
       'Jersey Number', 'Joined', 'Loaned From', 'Contract Valid Until',
       'Height', 'Weight', 'LS', 'ST', 'RS', 'LW', 'LF', 'CF', 'RF', 'RW',
       'LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LWB', 'LDM',
       'CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB', 'Crossing',
       'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling',
       'Curve', 'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',
       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',
       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',
       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',
       'Marking', 'StandingTackle', 'SlidingTackle', 'GKDiving', 'GKHandling',
       'GKKicking', 'GKPositioning', 'GKReflexes', 'Release Clause'],
      dtype='object')


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18207 entries, 0 to 18206
Data columns (total 89 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Unnamed: 0                18207 non-null  int64  
 1   ID                        18207 non-null  int64  
 2   Name                      18207 non-null  object 
 3   Age                       18207 non-null  int64  
 4   Photo                     18207 non-null  object 
 5   Nationality               18207 non-null  object 
 6   Flag                      18207 non-null  object 
 7   Overall                   18207 non-null  int64  
 8   Potential                 18207 non-null  int64  
 9   Club                      17966 non-null  object 
 10  Club Logo                 18207 non-null  object 
 11  Value                     18207 non-null  object 
 12  Wage                      18207 non-null  object 
 13  Special                   18207 non-null  int64  
 14  Preferred Foot            18159 non-null  object 
 15  International Reputation  18159 non-null  float64
 16  Weak Foot                 18159 non-null  float64
 17  Skill Moves               18159 non-null  float64
 18  Work Rate                 18159 non-null  object 
 19  Body Type                 18159 non-null  object 
 20  Real Face                 18159 non-null  object 
 21  Position                  18147 non-null  object 
 22  Jersey Number             18147 non-null  float64
 23  Joined                    16654 non-null  object 
 24  Loaned From               1264 non-null   object 
 25  Contract Valid Until      17918 non-null  object 
 26  Height                    18159 non-null  object 
 27  Weight                    18159 non-null  object 
 28  LS                        16122 non-null  object 
 29  ST                        16122 non-null  object 
 30  RS                        16122 non-null  object 
 31  LW                        16122 non-null  object 
 32  LF                        16122 non-null  object 
 33  CF                        16122 non-null  object 
 34  RF                        16122 non-null  object 
 35  RW                        16122 non-null  object 
 36  LAM                       16122 non-null  object 
 37  CAM                       16122 non-null  object 
 38  RAM                       16122 non-null  object 
 39  LM                        16122 non-null  object 
 40  LCM                       16122 non-null  object 
 41  CM                        16122 non-null  object 
 42  RCM                       16122 non-null  object 
 43  RM                        16122 non-null  object 
 44  LWB                       16122 non-null  object 
 45  LDM                       16122 non-null  object 
 46  CDM                       16122 non-null  object 
 47  RDM                       16122 non-null  object 
 48  RWB                       16122 non-null  object 
 49  LB                        16122 non-null  object 
 50  LCB                       16122 non-null  object 
 51  CB                        16122 non-null  object 
 52  RCB                       16122 non-null  object 
 53  RB                        16122 non-null  object 
 54  Crossing                  18159 non-null  float64
 55  Finishing                 18159 non-null  float64
 56  HeadingAccuracy           18159 non-null  float64
 57  ShortPassing              18159 non-null  float64
 58  Volleys                   18159 non-null  float64
 59  Dribbling                 18159 non-null  float64
 60  Curve                     18159 non-null  float64
 61  FKAccuracy                18159 non-null  float64
 62  LongPassing               18159 non-null  float64
 63  BallControl               18159 non-null  float64
 64  Acceleration              18159 non-null  float64
 65  SprintSpeed               18159 non-null  float64
 66  Agility                   18159 non-null  float64
 67  Reactions                 18159 non-null  float64
 68  Balance                   18159 non-null  float64
 69  ShotPower                 18159 non-null  float64
 70  Jumping                   18159 non-null  float64
 71  Stamina                   18159 non-null  float64
 72  Strength                  18159 non-null  float64
 73  LongShots                 18159 non-null  float64
 74  Aggression                18159 non-null  float64
 75  Interceptions             18159 non-null  float64
 76  Positioning               18159 non-null  float64
 77  Vision                    18159 non-null  float64
 78  Penalties                 18159 non-null  float64
 79  Composure                 18159 non-null  float64
 80  Marking                   18159 non-null  float64
 81  StandingTackle            18159 non-null  float64
 82  SlidingTackle             18159 non-null  float64
 83  GKDiving                  18159 non-null  float64
 84  GKHandling                18159 non-null  float64
 85  GKKicking                 18159 non-null  float64
 86  GKPositioning             18159 non-null  float64
 87  GKReflexes                18159 non-null  float64
 88  Release Clause            16643 non-null  object 
dtypes: float64(38), int64(6), object(45)
memory usage: 12.4+ MB


df.describe()


import missingno as msno
msno.matrix(df, labels=True, sort="descending");
msno.bar(df)

<Axes: >


# import the required libraries 
import sweetviz as sv
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split

print("SweetViz Version : {}".format(sv.__version__))
# analyzing the dataset
report = sv.analyze(df)
# show the report in a form of an HTML file
report.show_html('Report.html')

SweetViz Version : 2.2.1

                                             |          | [  0%]   00:00 -> (? left)

Report Report.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.


df.shape

(18207, 89)


from dataprep.eda.missing import plot_missing
plot_missing(df)

  0%|          | 0/774 [00:00<?, ?it/s]

C:\Users\prose\anaconda3\Lib\site-packages\dask\core.py:121: RuntimeWarning: invalid value encountered in divide
  return func(*(_execute_task(a, cache) for a in args))


# ignore warnings :
import warnings
warnings.filterwarnings('ignore')


import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)


# for visualizations
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
plt.style.use('fivethirtyeight')

# to visualize missing data
import missingno as msno


# check for missing values
df.isnull().sum()

Unnamed: 0           0
ID                   0
Name                 0
Age                  0
Photo                0
                  ... 
GKHandling          48
GKKicking           48
GKPositioning       48
GKReflexes          48
Release Clause    1564
Length: 89, dtype: int64


# fill missing values for continuous variables for proper data visualization
df['ShortPassing'].fillna(df['ShortPassing'].mean(), inplace = True)
df['Volleys'].fillna(df['Volleys'].mean(), inplace = True)
df['Dribbling'].fillna(df['Dribbling'].mean(), inplace = True)
df['Curve'].fillna(df['Curve'].mean(), inplace = True)
df['FKAccuracy'].fillna(df['FKAccuracy'], inplace = True)
df['LongPassing'].fillna(df['LongPassing'].mean(), inplace = True)
df['BallControl'].fillna(df['BallControl'].mean(), inplace = True)
df['HeadingAccuracy'].fillna(df['HeadingAccuracy'].mean(), inplace = True)
df['Finishing'].fillna(df['Finishing'].mean(), inplace = True)
df['Crossing'].fillna(df['Crossing'].mean(), inplace = True)
df['Weight'].fillna('200lbs', inplace = True)
df['Contract Valid Until'].fillna(2019, inplace = True)
df['Height'].fillna("5'11", inplace = True)
df['Loaned From'].fillna('None', inplace = True)
df['Joined'].fillna('Jul 1, 2018', inplace = True)
df['Jersey Number'].fillna(8, inplace = True)
df['Body Type'].fillna('Normal', inplace = True)
df['Position'].fillna('ST', inplace = True)
df['Club'].fillna('No Club', inplace = True)
df['Work Rate'].fillna('Medium/ Medium', inplace = True)
df['Skill Moves'].fillna(df['Skill Moves'].median(), inplace = True)
df['Weak Foot'].fillna(3, inplace = True)
df['Preferred Foot'].fillna('Right', inplace = True)
df['International Reputation'].fillna(1, inplace = True)
df['Wage'].fillna('€200K', inplace = True)


df.fillna(0, inplace = True)


df['Preferred Foot'].value_counts()

Right    13996
Left      4211
Name: Preferred Foot, dtype: int64


#Soccer players have a certain active timeframe in their lives. The distribution of age is here
df['Age'].hist()
plt.title("Distribution of age of the players")

Text(0.5, 1.0, 'Distribution of age of the players')


x = df['Age']
plt.figure(figsize = (12, 8))
plt.style.use('ggplot')
ax = sns.distplot(x, bins = 20, kde = True, color='g')
ax.set_xlabel(xlabel = 'Age of the Players', fontsize = 16)
ax.set_title(label = 'Histogram for Age distribution of Players', fontsize = 20)
plt.show()


df['International Reputation'].value_counts()

1.0    16580
2.0     1261
3.0      309
4.0       51
5.0        6
Name: International Reputation, dtype: int64


labels = df['International Reputation'].value_counts().index
size = df['International Reputation'].value_counts()
explode = [0, 0.1, 0.1, 0, 0]
plt.pie(size, labels = labels, explode = explode, shadow = True, autopct='%1.1f%%',startangle = 90)
plt.title('Distribution of International Reputation of players', fontsize = 20)
plt.legend()
plt.show()


df['Work Rate'].value_counts()

Medium/ Medium    9858
High/ Medium      3173
Medium/ High      1690
High/ High        1015
Medium/ Low        850
High/ Low          699
Low/ Medium        449
Low/ High          439
Low/ Low            34
Name: Work Rate, dtype: int64


fig, ax = plt.subplots(figsize=(12,8))
graph = sns.countplot(ax=ax,x=df['Work Rate'], data=df, palette = 'PuBuGn_d')
graph.set_title('Work Rate of the Players', fontsize = 20)
graph.set_xticklabels(graph.get_xticklabels(), rotation=30)
for p in graph.patches:
    height = p.get_height()
    graph.text(p.get_x()+p.get_width()/2., height + 0.1,height ,ha="center")


labels = df['Work Rate'].value_counts().index
size = df['Work Rate'].value_counts()
explode = [0,0,0.1,0,0.1,0,0,0,0]
plt.pie(size, labels = labels, explode = explode, shadow = True, autopct='%1.1f%%',startangle = 90)
plt.title('Distribution of Work Rate of players', fontsize = 20)
plt.legend()
plt.show()


fig, ax = plt.subplots(figsize=(12,8))
graph = sns.countplot(ax=ax,x=df['Work Rate'], data=df, hue='Preferred Foot', palette = 'PuBuGn_d')
graph.set_title('Work Rate of Players segregated by Preferred Foot'  , fontsize = 20)
graph.set_xticklabels(graph.get_xticklabels(), rotation=30)
for p in graph.patches:
    height = p.get_height()
    graph.text(p.get_x()+p.get_width()/2., height + 0.1,height ,ha="center")


df['Skill Moves'].value_counts()

2.0    8613
3.0    6600
1.0    2026
4.0     917
5.0      51
Name: Skill Moves, dtype: int64


fig, ax = plt.subplots(figsize=(12,8))
graph = sns.countplot(ax=ax,x=df['Skill Moves'], data=df, palette = 'PuBuGn_d')
graph.set_title('Skill Moves of the Players', fontsize = 20)
graph.set_xticklabels(graph.get_xticklabels(), rotation=30)
for p in graph.patches:
    height = p.get_height()
    graph.text(p.get_x()+p.get_width()/2., height + 0.1,height ,ha="center")


fig, ax = plt.subplots(figsize=(12,8))
graph = sns.countplot(ax=ax,x=df['Skill Moves'], data=df, hue='Preferred Foot', palette = 'PuBuGn_d')
graph.set_title('Skill Moves of Players segregated by Preferred Foot'  , fontsize = 20)
graph.set_xticklabels(graph.get_xticklabels(), rotation=30)
for p in graph.patches:
    height = p.get_height()
    graph.text(p.get_x()+p.get_width()/2., height + 0.1,height ,ha="center")


#Special Score of the Players
x = df['Special']
plt.figure(figsize=(18,10))
ax = sns.distplot(x)
ax.set_xlabel(xlabel = "Player's Special Scores", fontsize = 16)
ax.set_ylabel(ylabel = 'Number of Players', fontsize = 16)
ax.set_title(label = 'Distribution of Players Special Scores', fontsize = 20)
plt.show()


# different positions acquired by the players 
df['Position'].value_counts()

ST     2212
GK     2025
CB     1778
CM     1394
LB     1322
RB     1291
RM     1124
LM     1095
CAM     958
CDM     948
RCB     662
LCB     648
LCM     395
RCM     391
LW      381
RW      370
RDM     248
LDM     243
LS      207
RS      203
RWB      87
LWB      78
CF       74
LAM      21
RAM      21
RF       16
LF       15
Name: Position, dtype: int64


df['Nationality'].nunique()

164


top_countries = df['Nationality'].value_counts().head(10)

top_countries

England        1662
Germany        1198
Spain          1072
Argentina       937
France          914
Brazil          827
Italy           702
Colombia        618
Japan           478
Netherlands     453
Name: Nationality, dtype: int64


#A word cloud of nationalities will help understand which nationalities are dominating. 
#For doing that we need to join all the nationalities and then make a word cloud.
nationality = " ".join(n for n in df['Nationality'])


from wordcloud import WordCloud
plt.figure(figsize=(10, 10))
wc = WordCloud().generate(nationality)
plt.imshow(wc, interpolation='bilinear')
plt.axis('off')
plt.show()


fig, ax = plt.subplots(figsize=(12,8))
x = top_countries.values
y = top_countries.index
ax.barh(y, x, align='center', color='green')
ax.invert_yaxis()  # labels read top-to-bottom
ax.set_xlabel('Number of Players')
ax.set_ylabel('Name of Countries', rotation=0)
ax.set_title('Top 10 Countries with most number of players')
plt.show()


top_countries_name = top_countries.index

top_countries_name

Index(['England', 'Germany', 'Spain', 'Argentina', 'France', 'Brazil', 'Italy',
       'Colombia', 'Japan', 'Netherlands'],
      dtype='object')


#Age distribution from top countries
df_country_age = df.loc[df['Nationality'].isin(top_countries_name) & df['Age']]
plt.figure(1 , figsize = (12,6))
sns.boxplot(x = 'Nationality' , y = 'Age' , data = df_country_age, palette='rocket')
plt.title('Age Distribution in top countries')
plt.xticks(rotation = 50)
plt.show()


#Overall Rating
df_country_rating = df.loc[df['Nationality'].isin(top_countries_name) & df['Overall']]
plt.figure(1 , figsize = (12,6))
sns.barplot(x = 'Nationality' , y = 'Overall' , data = df_country_rating, palette='spring')
plt.title('Overall Rating Distribution of Players in top countries')
plt.xticks(rotation = 50)
plt.show()


#Potential Rating
df_country_rating = df.loc[df['Nationality'].isin(top_countries_name) & df['Potential']]
plt.figure(1 , figsize = (12,6))
sns.barplot(x = 'Nationality' , y = 'Potential' , data = df_country_rating, palette='PuBuGn_d')
plt.title('Potential Rating Distribution of Players in top countries')
plt.xticks(rotation = 50)
plt.show()


#Analyse players based on club
df['Club'].nunique()

652


df['Club'].unique()

array(['FC Barcelona', 'Juventus', 'Paris Saint-Germain',
       'Manchester United', 'Manchester City', 'Chelsea', 'Real Madrid',
       'Atlético Madrid', 'FC Bayern München', 'Tottenham Hotspur',
       'Liverpool', 'Napoli', 'Arsenal', 'Milan', 'Inter', 'Lazio',
       'Borussia Dortmund', 'Vissel Kobe', 'Olympique Lyonnais', 'Roma',
       'Valencia CF', 'Guangzhou Evergrande Taobao FC', 'FC Porto',
       'FC Schalke 04', 'Beşiktaş JK', 'LA Galaxy', 'Sporting CP',
       'Real Betis', 'Olympique de Marseille', 'RC Celta',
       'Bayer 04 Leverkusen', 'Real Sociedad', 'Villarreal CF',
       'Sevilla FC', 'SL Benfica', 'AS Saint-Étienne', 'AS Monaco',
       'Leicester City', 'Atalanta', 'Grêmio', 'Atlético Mineiro',
       'RB Leipzig', 'Ajax', 'Dalian YiFang FC', 'Everton',
       'West Ham United', '1. FC Köln', 'TSG 1899 Hoffenheim',
       'Shanghai SIPG FC', 'OGC Nice', 'Al Nassr',
       'Wolverhampton Wanderers', 'Borussia Mönchengladbach',
       'Hertha BSC', 'SV Werder Bremen', 'Cruzeiro',
       'Athletic Club de Bilbao', 'Torino', 'Medipol Başakşehir FK',
       'Beijing Sinobo Guoan FC', 'Crystal Palace', 'PFC CSKA Moscow',
       'VfL Wolfsburg', 'Shakhtar Donetsk', 'Toronto FC',
       'Lokomotiv Moscow', 'Sassuolo', 'New York City FC', 'Fluminense',
       'PSV', 'Levante UD', 'Fulham', 'Watford', 'Atlanta United',
       'Montpellier HSC', 'Galatasaray SK', 'Fenerbahçe SK', 'SD Eibar',
       'Los Angeles FC', 'Sampdoria', 'Al Hilal', 'VfB Stuttgart',
       'SC Braga', 'River Plate', 'Deportivo Alavés', 'No Club',
       'Eintracht Frankfurt', 'Girona FC', 'Guangzhou R&F; FC', 'Burnley',
       'Stoke City', 'Southampton', 'Tianjin Quanjian FC', 'Getafe CF',
       'Beijing Renhe FC', 'Montreal Impact', 'Chievo Verona', 'Genoa',
       'Portland Timbers', 'Tigres U.A.N.L.', 'RCD Espanyol',
       'Hebei China Fortune FC', 'Cagliari', 'Chicago Fire', 'DC United',
       'Sagan Tosu', 'Dynamo Kyiv', 'Santos', 'Internacional',
       'América FC (Minas Gerais)', 'Independiente', 'Boca Juniors',
       'Cruz Azul', '1. FSV Mainz 05', 'Bournemouth', 'Spartak Moscow',
       'Racing Club', 'FC Augsburg', 'Fiorentina', 'FC Nantes',
       'Feyenoord', 'Club Brugge KV', 'Brighton & Hove Albion', 'Al Ahli',
       'Jiangsu Suning FC', 'SC Freiburg', 'PAOK', 'Stade Rennais FC',
       'Trabzonspor', 'SPAL', 'Portimonense SC', 'Olympiacos CFP',
       'Club Atlético Huracán', 'Kasimpaşa SK', 'Newcastle United',
       'Frosinone', 'Querétaro', 'KRC Genk', 'Hannover 96',
       'Stade Malherbe Caen', 'Godoy Cruz', 'Toulouse Football Club',
       'RSC Anderlecht', 'Huddersfield Town', 'CD Tondela',
       'Seattle Sounders FC', 'Hamburger SV', 'FC Red Bull Salzburg',
       'Rio Ave FC', 'FC Girondins de Bordeaux', 'Melbourne Victory',
       'Parma', 'FC Basel 1893', 'Al Wehda', 'BSC Young Boys', 'KAA Gent',
       'Al Ittihad', 'Standard de Liège', 'Shanghai Greenland Shenhua FC',
       'Colo-Colo', 'Junior FC', 'West Bromwich Albion',
       'RC Strasbourg Alsace', 'Göztepe SK', 'Deportivo Cali',
       'Deportivo Toluca', 'Bologna', 'Nagoya Grampus', 'Amiens SC',
       'Changchun Yatai FC', 'Club Atlético Lanús', 'Botafogo',
       'Club América', 'Udinese', 'Real Valladolid CF', 'CD Leganés',
       'Club Atlético Banfield', 'Celtic', 'Vitória Guimarães',
       'FC København', 'UD Las Palmas', 'Deportivo de La Coruña',
       'Universidad Católica', 'San Lorenzo de Almagro', 'Rayo Vallecano',
       'Monterrey', 'Columbus Crew SC', 'MKE Ankaragücü',
       'Guizhou Hengfeng FC', 'Swansea City', 'Tianjin TEDA FC',
       'Chongqing Dangdai Lifan FC SWM Team', 'AEK Athens', 'Al Taawoun',
       'Melbourne City FC', 'En Avant de Guingamp',
       'Akhisar Belediyespor', 'Foggia', 'LOSC Lille', '1. FC Nürnberg',
       'Clube Sport Marítimo', 'Real Sporting de Gijón', 'BB Erzurumspor',
       'Shandong Luneng TaiShan FC', 'Club Atlético Colón', 'Bahia',
       'Once Caldas', 'FC Groningen', 'Angers SCO', 'Paraná',
       'Antalyaspor', 'Minnesota United FC', 'Club León', 'Empoli',
       'VVV-Venlo', 'Leeds United', 'Viktoria Plzeň', 'Alanyaspor',
       'Atlético Paranaense', 'Derby County', 'Kawasaki Frontale',
       'Cardiff City', 'Aston Villa', 'Guadalajara', 'Dijon FCO',
       'Santos Laguna', 'Málaga CF', 'Vitória', 'Çaykur Rizespor',
       'U.N.A.M.', 'Nottingham Forest', 'Royal Antwerp FC',
       'Club Tijuana', 'Sport Club do Recife', 'Real Salt Lake',
       'AZ Alkmaar', 'SK Slavia Praha', 'Willem II', 'Middlesbrough',
       'Dinamo Zagreb', 'Club Atlas', 'Granada CF', 'Sydney FC',
       'Sporting Kansas City', 'SV Zulte-Waregem', 'Philadelphia Union',
       'Real Oviedo', 'Pachuca', 'Boavista FC', 'Atiker Konyaspor',
       'Kaizer Chiefs', 'GD Chaves', 'Palermo', 'Atlético Nacional',
       'Puebla FC', 'Perth Glory', 'Panathinaikos FC', 'FC Sion',
       'Vitória de Setúbal', 'New York Red Bulls', 'Al Shabab',
       'Monarcas Morelia', 'Albacete BP', 'Rangers FC', 'Sparta Praha',
       'Legia Warszawa', 'Urawa Red Diamonds', 'Rosario Central',
       'Stade de Reims', 'ADO Den Haag', 'Chapecoense', 'FC Midtjylland',
       'San Jose Earthquakes', 'Belgrano de Córdoba', 'Brescia',
       'Kashima Antlers', 'CD Everton de Viña del Mar',
       'Fortuna Düsseldorf', 'SD Huesca', 'Preston North End',
       'Club Atlético Talleres', 'Benevento', 'Vitesse',
       'Gimnasia y Esgrima La Plata', 'Houston Dynamo', 'Club Necaxa',
       'Norwich City', 'Holstein Kiel', 'Ettifaq FC', 'Kayserispor',
       '1. FC Heidenheim 1846', 'Brentford', 'Yeni Malatyaspor',
       'Lobos BUAP', 'Bursaspor', 'Ceará Sporting Club',
       'Sheffield United', 'FC Ingolstadt 04', 'Estudiantes de La Plata',
       'AIK', 'Queens Park Rangers', 'Suwon Samsung Bluewings',
       'Heart of Midlothian', 'Reading', 'FC Dallas', 'Heracles Almelo',
       'Venezia FC', 'CD Lugo', 'Henan Jianye FC', 'Orlando City SC',
       'CA Osasuna', 'NAC Breda', 'Livorno', 'Universidad de Chile',
       'Brøndby IF', 'Aberdeen', 'Defensa y Justicia', 'Atlético Tucumán',
       'Blackburn Rovers', 'SV Darmstadt 98', 'Moreirense FC',
       'Sanfrecce Hiroshima', 'CD Numancia', 'KV Oostende', 'FC Utrecht',
       'Vancouver Whitecaps FC', 'Odense Boldklub', 'SC Heerenveen',
       'Racing Club de Lens', 'Independiente Santa Fe',
       'Sporting de Charleroi', 'Millonarios FC', 'Sheffield Wednesday',
       'Perugia', 'Daegu FC', 'Vélez Sarsfield',
       'Grasshopper Club Zürich', 'Sivasspor', 'Nîmes Olympique',
       'Rosenborg BK', 'SK Sturm Graz', 'FC Metz',
       'CD Universidad de Concepción', 'Hellas Verona', 'Brisbane Roar',
       'CD Feirense', 'Hull City', 'Waasland-Beveren', 'Neuchâtel Xamax',
       'Real Zaragoza', 'CD Aves', 'Millwall', 'Unión de Santa Fe',
       'KAS Eupen', 'Cádiz CF', 'FC Tokyo', 'CD Tenerife',
       '1. FC Union Berlin', 'Al Fayha', 'AJ Auxerre',
       'Patriotas Boyacá FC', 'Molde FK', 'Bristol City', 'CD Nacional',
       'Sporting Lokeren', 'FC St. Pauli', 'Deportes Iquique',
       'Al Qadisiyah', 'Atlético Bucaramanga', 'Club Atlético Tigre',
       'FK Austria Wien', 'Patronato', 'Malmö FF', 'Kashiwa Reysol',
       'US Cremonese', 'VfL Bochum 1848', 'SK Rapid Wien',
       'KSV Cercle Brugge', 'Rionegro Águilas', 'Gimnàstic de Tarragona',
       'Lecce', 'Santa Clara', 'BK Häcken', 'New England Revolution',
       'Orlando Pirates', 'Atlético Huila', 'Western Sydney Wanderers',
       'Kalmar FF', 'Independiente Medellín', 'Fortuna Sittard',
       'Lech Poznań', 'Djurgårdens IF', 'CF Reus Deportiu', 'SK Brann',
       'Ulsan Hyundai FC', 'Sint-Truidense VV', 'Carpi', 'Al Fateh',
       'Royal Excel Mouscron', 'AC Ajaccio', 'PEC Zwolle', 'Sunderland',
       'Club Atlético Aldosivi', 'US Salernitana 1919', 'FC Lorient',
       'Argentinos Juniors', 'AD Alcorcón', 'Crotone', 'Excelsior',
       'KV Kortrijk', 'IFK Norrköping', 'Adelaide United',
       'FC St. Gallen', 'Tiburones Rojos de Veracruz', 'CD Palestino',
       'Jeju United FC', 'Deportes Tolima', 'Jeonbuk Hyundai Motors',
       'Birmingham City', 'América de Cali', 'La Equidad', 'Spezia',
       'Aalborg BK', 'Le Havre AC', 'Górnik Zabrze',
       'Central Coast Mariners', 'Wigan Athletic',
       'Jagiellonia Białystok', 'Cittadella', 'Hibernian', 'FC Lugano',
       'San Martín de San Juan', 'Strømsgodset IF', 'Júbilo Iwata',
       "Newell's Old Boys", 'Al Faisaly', 'Colorado Rapids',
       'IF Elfsborg', 'SV Sandhausen', 'Al Batin', 'Stade Brestois 29',
       'UD Almería', 'Gyeongnam FC', 'Yokohama F. Marinos', 'Kilmarnock',
       'Pescara', 'Newcastle Jets', 'Córdoba CF', 'RCD Mallorca',
       'Hammarby IF', 'Cerezo Osaka', 'KFC Uerdingen 05',
       'Shimizu S-Pulse', 'MSV Duisburg', 'Os Belenenses',
       'DSC Arminia Bielefeld', 'Ipswich Town', 'FC Seoul',
       'Lechia Gdańsk', 'Gamba Osaka', 'CF Rayo Majadahonda', 'LASK Linz',
       'Bolton Wanderers', 'Al Raed', 'Extremadura UD', 'SC Paderborn 07',
       'Wellington Phoenix', 'Unión Española', 'Alianza Petrolera',
       'Cracovia', 'Gangwon FC', 'Elche CF', 'ESTAC Troyes', 'AS Béziers',
       'La Berrichonne de Châteauroux', 'Clermont Foot 63',
       '1. FC Magdeburg', 'Pohang Steelers', 'Örebro SK', 'Arka Gdynia',
       'SG Dynamo Dresden', 'SpVgg Greuther Fürth', 'CD Huachipato',
       'Wisła Kraków', 'Stabæk Fotball', 'Eintracht Braunschweig',
       'Valenciennes FC', 'FC Thun', 'San Luis de Quillota',
       ' SSV Jahn Regensburg', 'Cosenza', 'FC Nordsjælland',
       'FC Erzgebirge Aue', 'Jeonnam Dragons', 'Wolfsberger AC',
       'Chamois Niortais Football Club', 'Club Deportes Temuco',
       'AS Nancy Lorraine', 'Red Star FC', 'Al Hazem', 'Pogoń Szczecin',
       'Charlton Athletic', 'Grenoble Foot 38', 'FC Hansa Rostock',
       'San Martin de Tucumán', 'Incheon United FC', 'Śląsk Wrocław',
       'GFC Ajaccio', '1. FC Kaiserslautern', 'Deportivo Pasto',
       'Lincoln City', 'Motherwell', 'Rotherham United', 'Burton Albion',
       'Wisła Płock', 'FC Wacker Innsbruck', 'Peterborough United',
       'Ascoli', 'FC Zürich', 'Fleetwood Town', 'Padova',
       'FC Sochaux-Montbéliard', 'SV Wehen Wiesbaden', 'Unión La Calera',
       'Scunthorpe United', "CD O'Higgins", 'CD Antofagasta',
       'Plymouth Argyle', 'Aarhus GF', 'Lillestrøm SK', 'Karlsruher SC',
       'GIF Sundsvall', 'FC Emmen', 'Barnsley', 'Audax Italiano',
       'V-Varen Nagasaki', 'Paris FC', 'SpVgg Unterhaching', 'Hobro IK',
       'De Graafschap', 'Hokkaido Consadole Sapporo', 'Tromsø IL',
       'FC Luzern', 'FK Haugesund', 'Zagłębie Lubin', 'VfR Aalen',
       'Dundalk', 'Oxford United', 'Piast Gliwice', 'Ohod Club',
       'Östersunds FK', 'Vegalta Sendai', 'Crawley Town',
       'FC Admira Wacker Mödling', 'Vålerenga Fotball', 'Dundee FC',
       'Portsmouth', 'Envigado FC', 'Miedź Legnica', 'Odds BK',
       'SC Fortuna Köln', 'US Orléans Loiret Football', 'Sarpsborg 08 FF',
       'Jaguares de Córdoba', 'Bradford City', 'Accrington Stanley',
       'St. Johnstone FC', 'Boyacá Chicó FC', 'Luton Town',
       'SV Mattersburg', 'Kristiansund BK', 'Sangju Sangmu FC',
       'Rochdale', 'Walsall', 'Korona Kielce', 'Shonan Bellmare',
       'FC Würzburger Kickers', 'FSV Zwickau', 'St. Mirren', 'AC Horsens',
       'Esbjerg fB', 'HJK Helsinki', 'Southend United', 'Bristol Rovers',
       'Hamilton Academical FC', 'TSV 1860 München', 'Curicó Unido',
       'SCR Altach', 'Ranheim Fotball', 'Stevenage',
       'SG Sonnenhof Großaspach', 'Oldham Athletic', 'Milton Keynes Dons',
       'FK Bodø/Glimt', 'SC Preußen Münster', 'Wycombe Wanderers',
       'Vejle Boldklub', 'Bury', 'Randers FC', 'VfL Osnabrück',
       'SønderjyskE', 'IFK Göteborg', 'Mansfield Town', 'Coventry City',
       'Waterford FC', 'Shrewsbury', 'IK Start', 'Gillingham',
       'FC Energie Cottbus', 'FC Carl Zeiss Jena', 'Hallescher FC',
       'SV Meppen', 'AFC Wimbledon', 'Blackpool', 'Doncaster Rovers',
       'Sandefjord Fotball', 'VfL Sportfreunde Lotte', 'Cheltenham Town',
       'IK Sirius', 'Vendsyssel FF', 'Swindon Town', 'Notts County',
       'SKN St. Pölten', 'Exeter City', 'Northampton Town',
       'Shamrock Rovers', 'Colchester United', 'Livingston FC',
       'TSV Hartberg', 'Tranmere Rovers', 'Cambridge United',
       'Grimsby Town', 'Port Vale', 'Itagüí Leones FC',
       'Forest Green Rovers', 'Dalkurd FF', 'Zagłębie Sosnowiec',
       'Carlisle United', 'Trelleborgs FF', "St. Patrick's Athletic",
       'Morecambe', 'Cork City', 'IF Brommapojkarna', 'Crewe Alexandra',
       'Yeovil Town', 'Bohemian FC', 'Macclesfield Town',
       'Newport County', 'Sligo Rovers', 'Derry City', 'Limerick FC',
       'Bray Wanderers'], dtype=object)


clubs = ['FC Barcelona','Real Madrid','Juventus','Liverpool','Manchester United',
         'Chelsea','Arsenal','Paris Saint-Germain' ,'FC Bayern München','Manchester City']


#Age distribution in famous clubs
df_club_age = df.loc[df['Club'].isin(clubs) & df['Age']]
plt.figure(1 , figsize = (12,6))
sns.boxplot(x = 'Club', y = 'Age' , data = df_club_age, palette='spring')
plt.title('Age Distribution in famous clubs')
plt.xticks(rotation = 50)
plt.show()


#Overall Rating in famous clubs
df_club_rating = df.loc[df['Club'].isin(clubs) & df['Overall']]
plt.figure(1 , figsize = (12,6))
sns.boxplot(x = 'Club' , y = 'Overall' , data = df_club_rating, palette='PuBuGn_d')
plt.title('Overall Rating Distribution in famous clubs')
plt.xticks(rotation = 50)
plt.show()


#The Best Clubs with Players Overall Rating
best_dict = {}
for club in df['Club'].unique():
    overall_rating = df['Overall'][df['Club'] == club].sum()
    best_dict[club] = overall_rating
best_club = pd.DataFrame.from_dict(best_dict, orient='index', columns = ['overall'])
best_club['club'] = best_club.index
best_club = best_club.sort_values(by = 'overall', ascending =  False)

plt.figure(1 , figsize = (15 , 6))
sns.barplot(x ='club',y ='overall',data = best_club.head(10),palette='Reds')  
plt.xticks(rotation = 70)
plt.xlabel("Club")
plt.ylabel('Sum of Overall Rating of players in club')
plt.title('Clubs with best Players (sum of overall ratings of players per club)')
plt.ylim(2450 , 2600)
plt.show()


#Profiling top players
#The Best Players
df_best_players = pd.DataFrame.copy(df.sort_values(by ='Overall',ascending = False ).head(10))
plt.figure(1,figsize = (12,6))
sns.barplot(x ='Name' , y = 'Overall' , data = df_best_players, palette='PuBuGn_d')
plt.ylim(85,95)
plt.show()


#The Highest Earners
def normalizing_wage(x):
    if '€' in str(x) and 'M' in str(x):
        c = str(x).replace('€' , '')
        c = str(c).replace('M' , '')
        c = float(c) * 1000000

    else:
        c = str(x).replace('€' , '')
        c = str(c).replace('K' , '')
        c = float(c) * 1000

    return c

df['Normalized_Wage'] = df['Wage'].apply(lambda x : normalizing_wage(x))
df.sort_values(by = 'Normalized_Wage' , ascending = False)[['Name','Club','Nationality','Overall',
'Age','Normalized_Wage','Wage]].head(5)


#The Eldest Players
df.sort_values(by = 'Age' , ascending = False)[['Name','Club','Nationality','Overall', 'Age' ]].head()


#The Youngest Players
df.sort_values(by = 'Age' , ascending = True)[['Name','Club','Nationality','Overall', 'Age' ]].head()


#The Best Freekick Takers
df.sort_values(by = 'FKAccuracy' , ascending = False)[['Name','Club','Nationality','Age','FKAccuracy']].head()


#The Best Penalty Kick Taker
df.sort_values(by = 'Penalties' , ascending = False)[['Name','Club','Nationality','Age','Penalties']].head()


#Best players with the Ball Control
df.sort_values(by = 'BallControl' , ascending = False)[['Name','Club','Nationality','Overall', 'Age','BallControl']].head()


#Fastest Players
df.sort_values(by = 'SprintSpeed' , ascending  False)[['Name','Club','Nationality','Overall', 'Age','SprintSpeed']].head()


#The Best Dribbler
df.sort_values(by = 'Dribbling' , ascending = False)[['Name','Club','Nationality','Overall', 'Age','Dribbling']].head()


#The Best Finisher
df.sort_values(by = 'Finishing' , ascending = False)[['Name','Club','Nationality','Overall', 'Age','Finishing']].head()


#Distribution of weak foot
df['Weak Foot'].value_counts()

3.0    11397
2.0     3761
4.0     2662
5.0      229
1.0      158
Name: Weak Foot, dtype: int64


labels = df['Weak Foot'].value_counts().index
size = df['Weak Foot'].value_counts()
colors=['cyan','pink','orange','lightgreen','yellow']
explode = [0, 0.1, 0.1, 0, 0]
plt.pie(size, labels = labels, colors = colors, explode = explode, shadow = True, autopct='%1.1f%%',startangle = 90)
plt.title('Distribution of Weak Foot among players', fontsize = 20)
plt.legend()
plt.show()


df['Preferred Foot'].value_counts()/len(df)

Right    0.768715
Left     0.231285
Name: Preferred Foot, dtype: float64


labels = df['Preferred Foot'].value_counts().index
size = df['Preferred Foot'].value_counts()
colors=['cyan','pink']
plt.pie(size, labels = labels, colors = colors, shadow = True, autopct='%1.1f%%',startangle = 90)
plt.title('Distribution of Preferred Foot among players', fontsize = 20)
plt.legend()
plt.show()


#Segregation of Indian Players
def country(x):
    return df[df['Nationality'] == x].head()


# prepare dataset for Indian players
country('India')


sns.lineplot(data=df, x="Age", y="Overall")

<Axes: xlabel='Age', ylabel='Overall'>


sns.lineplot(data=df, x="Age", y="Overall")

<Axes: xlabel='Age', ylabel='Overall'>


df['Nationality'].value_counts()[0:10]

England        1662
Germany        1198
Spain          1072
Argentina       937
France          914
Brazil          827
Italy           702
Colombia        618
Japan           478
Netherlands     453
Name: Nationality, dtype: int64


import matplotlib.pyplot as plt

# Create a figure
plt.figure(figsize=(8, 5))

# Select the top 5 Nationalities and create a bar plot
top_nationalities = df['Nationality'].value_counts().head(5)
plt.bar(top_nationalities.index, top_nationalities, color="g")

# Show the plot
plt.show()


#Finding Out Which Player Gets The Highest Wages.
player_salary = df[['Name' , 'Wage']]
player_salary.head()


import matplotlib.pyplot as plt

# Create a figure with a specified size
plt.figure(figsize=(8, 5))

# Define the data you want to plot as regular Python lists
players = player_salary['Name'][0:5]
wages = player_salary['Wage'][0:5]

# Create a bar chart
plt.bar(players, wages, color="red")

# Display the chart
plt.show()


#Germany
Germany = df[df['Nationality'] == 'Germany']
Germany.head()


#Finding  Out Who Is The Tallest German Player Or The Player Belongs To German Nationality
Germany.sort_values(by=[ 'Height'], ascending=False).head()


#Finding Out The German Player Who Has The Highest Weight
Germany.sort_values(by=[ 'Weight'], ascending=False).head()


#Finding  Out Who Are The Top Most Earning German Players.
Germany.sort_values(by=[ 'Wage'], ascending=False).head()


#Columns with NaN null values
df.columns[df.isna().any()]

Index([], dtype='object')


#checking for NaN values 
df.isna().any().any()

False


#Players who are goalkeepers
strickers_df=df[['Name','Position','Nationality','Club','Overall','Potential']]
strickers_df=strickers_df[strickers_df['Position'].str.contains(pat='GK')]
strickers_df.sort_values('Overall',ascending=False).head(20)


#Let's check how many players are here only from England
eng_players_df=df[df.Nationality=='England']
eng_players_df


print('Total number of players from England : {}'.format(eng_players_df.shape[0]))

Total number of players from England : 1662


clubs_df=df[['Club',]].drop_duplicates(subset='Club')
clubs_df


print('Toal Number of clubs : {}'.format(clubs_df.shape[0]))

Toal Number of clubs : 652


#Top 10 positions of maxmium players
df.Position.value_counts().head(10)

ST     2212
GK     2025
CB     1778
CM     1394
LB     1322
RB     1291
RM     1124
LM     1095
CAM     958
CDM     948
Name: Position, dtype: int64


import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline 
import seaborn as sns
sns.set_style("darkgrid")
#matplotlib.rcParams['font.size'] = 14
#matplotlib.rcParams['figure.figsize'] = (20, 10)
#matplotlib.rcParams['figure.facecolor'] = '#00000000'


#Clustering Players by Overall and Potential Rating
player_highest_overall_df=df[['Name','Overall','Potential','Age','Nationality','Club','Preferred Foot']].copy()
player_highest_overall_df=player_highest_overall_df.sort_values('Overall',ascending=False)
player_highest_overall_df


#Top 10 players with highest overall rating
top_10_overall_df=player_highest_overall_df.head(10)
top_10_overall_df


player_highest_potential_df=df[['Name','Potential','Overall','Age','Nationality','Club','Preferred Foot']].copy()
player_highest_potential_df=player_highest_potential_df.sort_values('Potential',ascending=False)
player_highest_potential_df


##Top 10 players with highest potential rating
top_10_potential_df=player_highest_potential_df.head(10)
top_10_potential_df


#Top 10 players with potential rating + Top 10 players with overall rating
top_10_combined_df=pd.concat([top_10_potential_df,top_10_overall_df], axis=0)
top_10_combined_df=top_10_combined_df.drop_duplicates(subset='Name')
combined_df=top_10_combined_df.groupby('Name')[['Overall','Potential']].max()
combined_df=combined_df.sort_values('Overall',ascending=True)
combined_df


combined_df.plot(kind='barh',figsize=(15,10))
plt.title('Overall Rating vs Potential Rating ',fontsize=25)
plt.xlim(0,110,10)
plt.xlabel('Name')
plt.ylabel('Rating')
plt.legend();


#Players Overall and Potential Rating Distribution
plt.figure(figsize=(10,5))
plt.hist([df.Overall,df.Potential],color=['blue','black'],alpha=0.4,stacked=True)
plt.title('Players Overall and Potential Rating Distribution',fontsize=20)
plt.xlabel('Rating',fontsize=20)
plt.ylabel('Players Frequency',fontsize=20)
plt.legend(['overall','potential'],fontsize=20);


#Clustering players By Nationality
#Players number counts country wise
country_count_df=df['Nationality'].value_counts()
country_count_df

England                 1662
Germany                 1198
Spain                   1072
Argentina                937
France                   914
                        ... 
New Caledonia              1
Fiji                       1
São Tomé & Príncipe        1
United Arab Emirates       1
Botswana                   1
Name: Nationality, Length: 164, dtype: int64


#Let's plot Top 20 countries with maximum number of players in FIFA 21
country_df=pd.DataFrame(country_count_df.head(20))
country_df.sort_values('Nationality',ascending=True).plot(kind='barh',figsize=(15,10),color='lightgreen')
plt.title('Top 20 Countries With Maximum Number of Players in FIFA 21',fontsize=25)
plt.ylabel('Countries',fontsize=20)
plt.xlabel('Number of Players',fontsize=20)
plt.legend(['Number of Players']);


#Clustering players by International Reputation
rep_df=df[['Name','International Reputation']].sort_values('International Reputation',ascending=False)
rep_df


#Top 10 players with respect to their international reputation
rep_df.head(10)


#Representation of The Distribution of International Reputation of Players in FIFA 21
#Plotting a pie chart to represent the distribution of international reputation of players in FIFA 21
labels=['1','2','3','4','5']
sizes=df['International Reputation'].value_counts()
colors=['green','blue','yellow','red','black']
explode=[0.1,0.1,0.2,0.5,0.9]
plt.rcParams['figure.figsize']=(10,10)
plt.pie(sizes,labels=labels,colors=colors,explode=explode,shadow=True)
plt.title('International Reputation of The Players in FIFA 21',fontsize=20)
plt.legend()
plt.show()


v=df['International Reputation'].value_counts()
print('Out of all players, {:.2f}% players have international reputation of 1 in FIFA 21'.format(v.iloc[0]/df.shape[0]*100))

Out of all players, 91.06% players have international reputation of 1 in FIFA 21


#Each Different Playing Positions Distribution in FIFA 21
#printing total different positions
c=0
for i in df.Position.unique():
    if ',' not in i:
        print(i)
        c+=1
print('\n\nTotal {} different playing positions.'.format(c))

RF
ST
LW
GK
RCM
LF
RS
RCB
LCM
CB
LDM
CAM
CDM
LS
LCB
RM
LAM
LM
LB
RDM
RW
CM
RB
RAM
CF
RWB
LWB


Total 27 different playing positions.


#Taking each different position players in each seperate dataframes
plis=[df[df.Position.str[-2:]=='GK'].value_counts().sum(),
df[df.Position.str[-2:]=='ST'].value_counts().sum(),
df[df.Position.str[-2:]=='CB'].value_counts().sum(),
df[df.Position.str[-2:]=='LW'].value_counts().sum(),
df[df.Position.str[-2:]=='RW'].value_counts().sum(),
df[df.Position.str[-3:]=='CDM'].value_counts().sum(),
df[df.Position.str[-2:]=='CM'].value_counts().sum(),
df[df.Position.str[-2:]=='RB'].value_counts().sum(),
df[df.Position.str[-2:]=='LB'].value_counts().sum(),
df[df.Position.str[-2:]=='CF'].value_counts().sum(),
df[df.Position.str[-2:]=='LM'].value_counts().sum(),
df[df.Position.str[-2:]=='RM'].value_counts().sum(),
df[df.Position.str[-3:]=='LWB'].value_counts().sum(),
df[df.Position.str[-3:]=='CAM'].value_counts().sum(),
df[df.Position.str[-3:]=='RWB'].value_counts().sum()]


sns.set_theme()
matplotlib.rcParams['font.size'] = 14
matplotlib.rcParams['figure.figsize'] = (15, 8)


from IPython.display import display
with pd.option_context('display.max_columns',None):
    display(df.describe())


#Filling the missing value for the continuous variables for proper data visualization
df['ShortPassing'].fillna(df['ShortPassing'].mean(), inplace = True)
df['Volleys'].fillna(df['Volleys'].mean(), inplace = True)
df['Dribbling'].fillna(df['Dribbling'].mean(), inplace = True)
df['Curve'].fillna(df['Curve'].mean(), inplace = True)
df['FKAccuracy'].fillna(df['FKAccuracy'], inplace = True)
df['LongPassing'].fillna(df['LongPassing'].mean(), inplace = True)
df['BallControl'].fillna(df['BallControl'].mean(), inplace = True)
df['HeadingAccuracy'].fillna(df['HeadingAccuracy'].mean(), inplace = True)
df['Finishing'].fillna(df['Finishing'].mean(), inplace = True)
df['Crossing'].fillna(df['Crossing'].mean(), inplace = True)
df['Weight'].fillna('200lbs', inplace = True)
df['Contract Valid Until'].fillna(2019, inplace = True)
df['Height'].fillna("5'11", inplace = True)
df['Loaned From'].fillna('None', inplace = True)
df['Joined'].fillna('Jul 1, 2018', inplace = True)
df['Jersey Number'].fillna(8, inplace = True)
df['Body Type'].fillna('Normal', inplace = True)
df['Position'].fillna('ST', inplace = True)
df['Club'].fillna('No Club', inplace = True)
df['Work Rate'].fillna('Medium/ Medium', inplace = True)
df['Skill Moves'].fillna(df['Skill Moves'].median(), inplace = True)
df['Weak Foot'].fillna(3, inplace = True)
df['Preferred Foot'].fillna('Right', inplace = True)
df['International Reputation'].fillna(1, inplace = True)
df['Wage'].fillna('€200K', inplace = True)
df.fillna(0, inplace = True)


def defending(df):
    return int(round((df[['Marking', 'StandingTackle', 
                               'SlidingTackle']].mean()).mean()))

def general(df):
    return int(round((df[['HeadingAccuracy', 'Dribbling', 'Curve', 
                               'BallControl']].mean()).mean()))

def mental(df):
    return int(round((df[['Aggression', 'Interceptions', 'Positioning', 
                               'Vision','Composure']].mean()).mean()))

def passing(df):
    return int(round((df[['Crossing', 'ShortPassing', 
                               'LongPassing']].mean()).mean()))

def mobility(df):
    return int(round((df[['Acceleration', 'SprintSpeed', 
                               'Agility','Reactions']].mean()).mean()))
def power(df):
    return int(round((df[['Balance', 'Jumping', 'Stamina', 
                               'Strength']].mean()).mean()))

def rating(df):
    return int(round((df[['Potential', 'Overall']].mean()).mean()))

def shooting(df):
    return int(round((df[['Finishing', 'Volleys', 'FKAccuracy', 
                               'ShotPower','LongShots', 'Penalties']].mean()).mean()))


#Defining a function for cleaning the Weight data
def extract_value_from(value):
  out = value.replace('lbs', '')
  return float(out)

# applying the function to weight column
#data['value'] = data['value'].apply(lambda x: extract_value_from(x))
df['Weight'] = df['Weight'].apply(lambda x : extract_value_from(x))

df['Weight'].head()

0    159.0
1    183.0
2    150.0
3    168.0
4    154.0
Name: Weight, dtype: float64


#Defining a function for cleaning the wage column
def extract_value_from(Value):
    out = Value.replace('€', '')
    if 'M' in out:
        out = float(out.replace('M', ''))*1000000
    elif 'K' in Value:
        out = float(out.replace('K', ''))*1000
    return float(out)


#Skill Moves of Players
plt.figure(figsize = (10, 8))
ax = sns.countplot(x = 'Skill Moves', data = df, palette = 'pastel')
ax.set_title(label = 'Count of players on Basis of their skill moves', fontsize = 20)
ax.set_xlabel(xlabel = 'Number of Skill Moves', fontsize = 16)
ax.set_ylabel(ylabel = 'Count', fontsize = 16)
plt.show()


#Height of Players
plt.figure(figsize = (13, 8))
ax = sns.countplot(x = 'Height', data = df, palette = 'dark')
ax.set_title(label = 'Count of players on Basis of Height', fontsize = 20)
ax.set_xlabel(xlabel = 'Height in Foot per inch', fontsize = 16)
ax.set_ylabel(ylabel = 'Count', fontsize = 16)
plt.show()


# Checking for records that contain missing values
null_val = df[df.isnull().any(axis = 1)]
null_val


# Overall rating distribution 
plt.figure(figsize=(15,8))
sns.distplot(df['Overall'], bins=15, color='r') 
plt.title('Overall Rating Distribution in FIFA 23', fontsize = 16)
plt.show()


#Correlation Heatmap
#Next up, we want to see the correlation among the 
#relevant player attributes (Overall Rating, Potential Rating, Value, Age, Height, Weight, Wage, Release Clause). 
#Correlation matrices give such valuable insights as they show which attribute is influenced by the other. We will first of all,
#compute the correlation matrix of these relevant attributes.
# compute correlation matrix of the relevant columns 
relevant_col = df[['Overall', 'Potential', 'Age']].corr()
corr = relevant_col.corr()
corr


# Display correlation heatmap
plt.figure(figsize=(15,10))
mask= np.triu(np.ones_like(corr, dtype=bool))
sns.heatmap(corr, annot=True, mask=mask, cmap= 'vlag', center=0)

<Axes: >


#Age vs Overall Rating
#Now we’re going to create a scatter plot showing 
#the relationship between Age and Overall Rating.
# Age vs Overall rating
plt.figure(figsize=(15,8))
sns.scatterplot(x = 'Age', y = 'Overall', data = df)
plt.title('Age vs Overall Rating', fontsize = 15)
plt.xlabel('Age', fontsize = 15)
plt.ylabel('Overall Rating', fontsize = 15)

Text(0, 0.5, 'Overall Rating')


# The Top 10 ranked by Overall rating 
top_ten_overall = df[['Name','Overall','Age']].nlargest(10, 'Overall')

top_ten_overall


#Count of Players on Basis of Age
df['Age'].value_counts()

21    1423
26    1387
24    1358
22    1340
23    1332
25    1319
20    1240
27    1162
28    1101
19    1024
29     959
30     917
18     732
31     707
32     574
33     408
34     404
17     289
35     196
36     127
37      82
16      42
38      37
39      25
40      13
41       5
44       2
45       1
42       1
Name: Age, dtype: int64


plt.figure(figsize =(15,7))
sns.countplot(x = 'Age', data = df)
plt.title('Count of Players on the Basis of Age (FIFA 23)', fontsize = 16)
plt.xlabel('Age', fontsize = 16)
plt.ylabel('Count', fontsize = 16)

Text(0, 0.5, 'Count')


library("DALEX")
df_gbm_exp_deep <- DALEX::explain(df_gbm_deep, 
        df = fifa_small, y = 10^fifa_small$LogValue, 
        predict_function = function(m,x) 10^predict(m, x, n.trees = 250),
        label = "GBM deep")
from lightgbm import LGBMRegressor
from sklearn.model_selection import train_test_split
import numpy as np

X = df.drop(["Nationality", "Overall", "Potential", 
     "Value", "Wage"], axis = 1)
y = df['Value']
ylog = np.log(y)

X_train, X_test, ylog_train, ylog_test, y_train, y_test =
     train_test_split(X, ylog, y, test_size = 0.25, random_state = 4)
gbm_model = LGBMRegressor()
gbm_model.fit(X_train, ylog_train, verbose = False)

  Cell In[207], line 2
    df_gbm_exp_deep <- DALEX::explain(df_gbm_deep,
                            ^
SyntaxError: invalid syntax


df.describe().apply(lambda s: s.apply(lambda x: format(x,
'f')))


player_name = df[["Acceleration","Name","Position","Age" , "Nationality","SprintSpeed"]].nlargest(7, ["Acceleration"]).set_index("Name")
player_name


#Players Overall and Potential Rating vs Age in FIFA 21
fig, axes = plt.subplots(1, 2, sharex=True, figsize=(18,10))
fig.suptitle('Overall and Potential Rating vs Age',fontsize=20)
axes[0].set_title('Overall vs Age')
axes[1].set_title('Potential vs Age')
sns.scatterplot(ax=axes[0],x=df.Age,y=df.Overall);
sns.scatterplot(x=df.Age,y=df.Potential);


# Removing spaces in the column names to enable easy column reference  
df.columns = df.columns.str.replace(' ','')


df.head(5)


df.tail()


df.info

<bound method DataFrame.info of        Unnamed:0      ID                Name  Age  \
0              0  158023            L. Messi   31   
1              1   20801   Cristiano Ronaldo   33   
2              2  190871           Neymar Jr   26   
3              3  193080              De Gea   27   
4              4  192985        K. De Bruyne   27   
...          ...     ...                 ...  ...   
18202      18202  238813        J. Lundstram   19   
18203      18203  243165  N. Christoffersson   19   
18204      18204  241638           B. Worman   16   
18205      18205  246268      D. Walker-Rice   17   
18206      18206  246269           G. Nugent   16   

                                                Photo Nationality  \
0      https://cdn.sofifa.org/players/4/19/158023.png   Argentina   
1       https://cdn.sofifa.org/players/4/19/20801.png    Portugal   
2      https://cdn.sofifa.org/players/4/19/190871.png      Brazil   
3      https://cdn.sofifa.org/players/4/19/193080.png       Spain   
4      https://cdn.sofifa.org/players/4/19/192985.png     Belgium   
...                                               ...         ...   
18202  https://cdn.sofifa.org/players/4/19/238813.png     England   
18203  https://cdn.sofifa.org/players/4/19/243165.png      Sweden   
18204  https://cdn.sofifa.org/players/4/19/241638.png     England   
18205  https://cdn.sofifa.org/players/4/19/246268.png     England   
18206  https://cdn.sofifa.org/players/4/19/246269.png     England   

                                      Flag  Overall  Potential  \
0      https://cdn.sofifa.org/flags/52.png       94         94   
1      https://cdn.sofifa.org/flags/38.png       94         94   
2      https://cdn.sofifa.org/flags/54.png       92         93   
3      https://cdn.sofifa.org/flags/45.png       91         93   
4       https://cdn.sofifa.org/flags/7.png       91         92   
...                                    ...      ...        ...   
18202  https://cdn.sofifa.org/flags/14.png       47         65   
18203  https://cdn.sofifa.org/flags/46.png       47         63   
18204  https://cdn.sofifa.org/flags/14.png       47         67   
18205  https://cdn.sofifa.org/flags/14.png       47         66   
18206  https://cdn.sofifa.org/flags/14.png       46         66   

                      Club  ... Marking  StandingTackle  SlidingTackle  \
0             FC Barcelona  ...    33.0            28.0           26.0   
1                 Juventus  ...    28.0            31.0           23.0   
2      Paris Saint-Germain  ...    27.0            24.0           33.0   
3        Manchester United  ...    15.0            21.0           13.0   
4          Manchester City  ...    68.0            58.0           51.0   
...                    ...  ...     ...             ...            ...   
18202      Crewe Alexandra  ...    40.0            48.0           47.0   
18203       Trelleborgs FF  ...    22.0            15.0           19.0   
18204     Cambridge United  ...    32.0            13.0           11.0   
18205      Tranmere Rovers  ...    20.0            25.0           27.0   
18206      Tranmere Rovers  ...    40.0            43.0           50.0   

       GKDiving GKHandling  GKKicking  GKPositioning  GKReflexes  \
0           6.0       11.0       15.0           14.0         8.0   
1           7.0       11.0       15.0           14.0        11.0   
2           9.0        9.0       15.0           15.0        11.0   
3          90.0       85.0       87.0           88.0        94.0   
4          15.0       13.0        5.0           10.0        13.0   
...         ...        ...        ...            ...         ...   
18202      10.0       13.0        7.0            8.0         9.0   
18203      10.0        9.0        9.0            5.0        12.0   
18204       6.0        5.0       10.0            6.0        13.0   
18205      14.0        6.0       14.0            8.0         9.0   
18206      10.0       15.0        9.0           12.0         9.0   

      ReleaseClause Normalized_Wage  
0               NaN        565000.0  
1               NaN        405000.0  
2               8.1        290000.0  
3               NaN        260000.0  
4               NaN        355000.0  
...             ...             ...  
18202           NaN          1000.0  
18203           NaN          1000.0  
18204           NaN          1000.0  
18205           NaN          1000.0  
18206           NaN          1000.0  

[18207 rows x 90 columns]>


import warnings
import numpy as np
import pandas as pd
from pyod.models.mad import MAD
from pyod.models.knn import KNN
from pyod.models.lof import LOF
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest


import numpy as np 
import pandas as pd 

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.ensemble import IsolationForest
from sklearn.covariance import EllipticEnvelope
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
from sklearn.svm import OneClassSVM
from sklearn.neighbors import LocalOutlierFactor


#Parametric methods: Univariate
df.hist(figsize=(6,6));


# enough variation between features to show outliers
df.describe()


import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("/Users/prose/OneDrive/Desktop/Data/data.csv")
df.head()


# enough variation between features to show outliers
df.describe()


#Parametric methods: Univariate
df.hist(figsize=(6,6));


def out_std(s, nstd=3.0, return_thresholds=False):
    """
    Return a boolean mask of outliers for a series
    using standard deviation, works column-wise.
    param nstd:
        Set number of standard deviations from the mean
        to consider an outlier
    :type nstd: ``float``
    param return_thresholds:
        True returns the lower and upper bounds, good for plotting.
        False returns the masked array 
    :type return_thresholds: ``bool``
    """
    data_mean, data_std = s.mean(), s.std()
    cut_off = df_std * nstd
    lower, upper = df_mean - cut_off, df_mean + cut_off
    if return_thresholds:
        return lower, upper
    else:
        return [True if x < lower or x > upper else False for x in s]

def out_iqr(s, k=1.5, return_thresholds=False):
    """
    Return a boolean mask of outliers for a series
    using interquartile range, works column-wise.
    param k:
        some cutoff to multiply by the iqr
    :type k: ``float``
    param return_thresholds:
        True returns the lower and upper bounds, good for plotting.
        False returns the masked array 
    :type return_thresholds: ``bool``
    """
    # calculate interquartile range
    q25, q75 = np.percentile(s, 25), np.percentile(s, 75)
    iqr = q75 - q25
    # calculate the outlier cutoff
    cut_off = iqr * k
    lower, upper = q25 - cut_off, q75 + cut_off
    if return_thresholds:
        return lower, upper
    else: # identify outliers
        return [True if x < lower or x > upper else False for x in s]


df['Height'].describe()

count     18159
unique       21
top         6'0
freq       2881
Name: Height, dtype: object


df['Weight'].describe()

count      18159
unique        57
top       165lbs
freq        1483
Name: Weight, dtype: object


# Create a histogram using Seaborn
g = sns.histplot(data = df, x = 'Weight')

# Add labels
g.set_xlabel('Weight Distribution')

Text(0.5, 0, 'Weight Distribution')


# Create a histogram using Seaborn
g = sns.histplot(data = df, x = 'Height')

# Add labels
g.set_xlabel('Height Distribution')

Text(0.5, 0, 'Height Distribution')


df['Weight'] = df['Weight'].str.replace('lbs', '')


df['Value'] = df['Value'].str.replace('M', '')


df["Value"].head()

0    €110.5
1       €77
2    €118.5
3       €72
4      €102
Name: Value, dtype: object


df["Weight"].head()

0    159
1    183
2    150
3    168
4    154
Name: Weight, dtype: object


# Box Plot
import seaborn as sns
sns.boxplot(df['Weight'])

<Axes: >


import pandas as pd

# Use Regular Expression to convert Height from feet + inches into cm. Convert from text into integer
r = re.compile(r"([0-9]+)'([0-9]+)")    # to set the pattern e.g. 5'7 , 4'12 etc
def get_cm(height):
    height = str(height)
    m = r.match(height)
    if m == None:
        return float('NaN')
    else:
        return float(m.group(1))*30.48 + float(m.group(2))*2.54
df["Height"] = df["Height"].apply(lambda x:get_cm(x))


# Box Plot
import seaborn as sns
sns.boxplot(df['Height'])

<Axes: >


# Import zscore function
from scipy.stats import zscore

# Calculate z-score for each data point and compute its absolute value
z_scores = zscore(df['Height'])
abs_z_scores = np.abs(z_scores)

# Select the outliers using a threshold of 3
outliers = df[abs_z_scores > 3]
outliers.head()


# Import the median_abs_deviation function
from scipy.stats import median_abs_deviation

# Obtain the MAD value
mad_score = median_abs_deviation(df['Height'])
mad_score

nan


# Import the median_abs_deviation function
from scipy.stats import median_abs_deviation

# Obtain the MAD value
mad_score = median_abs_deviation(df['Age'])
mad_score

4.0


# Calculate the percentiles
seventy_fifth = df['Age'].quantile(0.75)
twenty_fifth = df['Age'].quantile(0.25)

# Obtain IQR
iqr = seventy_fifth - twenty_fifth

# Upper and lower thresholds
upper = seventy_fifth + (1.5 * iqr)
lower = twenty_fifth - (1.5 * iqr)

# Subset the dataset
outliers = df[(df['Age'] < lower) | (df['Age'] > upper)]
outliers.head()


df['Weight'].head()

0    159
1    183
2    150
3    168
4    154
Name: Weight, dtype: object


df['Age'].head()

0    31
1    33
2    26
3    27
4    27
Name: Age, dtype: int64


# Convert the 'Weight' column to int64
df['Weight'] = pd.to_numeric(df['Weight'], errors='coerce').astype('Int64')

# Display the DataFrame with 'Weight' column updated to int64
print(df)

       Unnamed: 0      ID                Name  Age  \
0               0  158023            L. Messi   31   
1               1   20801   Cristiano Ronaldo   33   
2               2  190871           Neymar Jr   26   
3               3  193080              De Gea   27   
4               4  192985        K. De Bruyne   27   
...           ...     ...                 ...  ...   
18202       18202  238813        J. Lundstram   19   
18203       18203  243165  N. Christoffersson   19   
18204       18204  241638           B. Worman   16   
18205       18205  246268      D. Walker-Rice   17   
18206       18206  246269           G. Nugent   16   

                                                Photo Nationality  \
0      https://cdn.sofifa.org/players/4/19/158023.png   Argentina   
1       https://cdn.sofifa.org/players/4/19/20801.png    Portugal   
2      https://cdn.sofifa.org/players/4/19/190871.png      Brazil   
3      https://cdn.sofifa.org/players/4/19/193080.png       Spain   
4      https://cdn.sofifa.org/players/4/19/192985.png     Belgium   
...                                               ...         ...   
18202  https://cdn.sofifa.org/players/4/19/238813.png     England   
18203  https://cdn.sofifa.org/players/4/19/243165.png      Sweden   
18204  https://cdn.sofifa.org/players/4/19/241638.png     England   
18205  https://cdn.sofifa.org/players/4/19/246268.png     England   
18206  https://cdn.sofifa.org/players/4/19/246269.png     England   

                                      Flag  Overall  Potential  \
0      https://cdn.sofifa.org/flags/52.png       94         94   
1      https://cdn.sofifa.org/flags/38.png       94         94   
2      https://cdn.sofifa.org/flags/54.png       92         93   
3      https://cdn.sofifa.org/flags/45.png       91         93   
4       https://cdn.sofifa.org/flags/7.png       91         92   
...                                    ...      ...        ...   
18202  https://cdn.sofifa.org/flags/14.png       47         65   
18203  https://cdn.sofifa.org/flags/46.png       47         63   
18204  https://cdn.sofifa.org/flags/14.png       47         67   
18205  https://cdn.sofifa.org/flags/14.png       47         66   
18206  https://cdn.sofifa.org/flags/14.png       46         66   

                      Club  ... Composure Marking StandingTackle  \
0             FC Barcelona  ...      96.0    33.0           28.0   
1                 Juventus  ...      95.0    28.0           31.0   
2      Paris Saint-Germain  ...      94.0    27.0           24.0   
3        Manchester United  ...      68.0    15.0           21.0   
4          Manchester City  ...      88.0    68.0           58.0   
...                    ...  ...       ...     ...            ...   
18202      Crewe Alexandra  ...      45.0    40.0           48.0   
18203       Trelleborgs FF  ...      42.0    22.0           15.0   
18204     Cambridge United  ...      41.0    32.0           13.0   
18205      Tranmere Rovers  ...      46.0    20.0           25.0   
18206      Tranmere Rovers  ...      43.0    40.0           43.0   

       SlidingTackle GKDiving  GKHandling  GKKicking  GKPositioning  \
0               26.0      6.0        11.0       15.0           14.0   
1               23.0      7.0        11.0       15.0           14.0   
2               33.0      9.0         9.0       15.0           15.0   
3               13.0     90.0        85.0       87.0           88.0   
4               51.0     15.0        13.0        5.0           10.0   
...              ...      ...         ...        ...            ...   
18202           47.0     10.0        13.0        7.0            8.0   
18203           19.0     10.0         9.0        9.0            5.0   
18204           11.0      6.0         5.0       10.0            6.0   
18205           27.0     14.0         6.0       14.0            8.0   
18206           50.0     10.0        15.0        9.0           12.0   

      GKReflexes Release Clause  
0            8.0        €226.5M  
1           11.0        €127.1M  
2           11.0        €228.1M  
3           94.0        €138.6M  
4           13.0        €196.4M  
...          ...            ...  
18202        9.0          €143K  
18203       12.0          €113K  
18204       13.0          €165K  
18205        9.0          €143K  
18206        9.0          €165K  

[18207 rows x 89 columns]


# Calculate the percentiles
seventy_fifth = df['Weight'].quantile(0.75)
twenty_fifth = df['Weight'].quantile(0.25)

# Obtain IQR
iqr = seventy_fifth - twenty_fifth

# Upper and lower thresholds
upper = seventy_fifth + (1.5 * iqr)
lower = twenty_fifth - (1.5 * iqr)

# Subset the dataset
outliers = df[(df['Weight'] < lower) | (df['Weight'] > upper)]
outliers.head()


# Remove rows with NA values in the 'Weight' column
df = df.dropna(subset=['Weight'])


# Import the median_abs_deviation function
from scipy.stats import median_abs_deviation

# Obtain the MAD value
mad_score = median_abs_deviation(df['Weight'])
mad_score

11.0


# Box Plot
import seaborn as sns
sns.boxplot(df['Weight'])

<Axes: >


# Create a box plot
g = sns.boxplot(data = df, x = 'Weight')

# Add a title and change xlabel
g.set_title('Box Plot of Weight')
g.set_xlabel('Weight')

Text(0.5, 0, 'Weight')


df['Height'].head()

0    170.18
1    187.96
2    175.26
3    193.04
4    180.34
Name: Height, dtype: float64


# Calculate the percentiles
seventy_fifth = df['Height'].quantile(0.75)
twenty_fifth = df['Height'].quantile(0.25)

# Obtain IQR
iqr = seventy_fifth - twenty_fifth

# Upper and lower thresholds
upper = seventy_fifth + (1.5 * iqr)
lower = twenty_fifth - (1.5 * iqr)

# Subset the dataset
outliers = df[(df['Height'] < lower) | (df['Height'] > upper)]
outliers.head()


# Import the median_abs_deviation function
from scipy.stats import median_abs_deviation

# Obtain the MAD value
mad_score = median_abs_deviation(df['Height'])
mad_score

5.079999999999984


# Import the median_abs_deviation function
from scipy.stats import median_abs_deviation

# Obtain the MAD value
mad_score = median_abs_deviation(df['Weight'])
mad_score

11.0


# Create a box plot
g = sns.boxplot(data = df, x = 'Height')

# Add a title and change xlabel
g.set_title('Box Plot of Height')
g.set_xlabel('Height')

Text(0.5, 0, 'Height')


# Import zscore function
from scipy.stats import zscore

# Calculate z-score for each data point and compute its absolute value
z_scores = zscore(df['Height'])
abs_z_scores = np.abs(z_scores)

# Select the outliers using a threshold of 3
outliers = df[abs_z_scores > 3]
outliers.head()


# Obtain number of outliers
print(f'Number of outliers: {len(outliers)}')

Number of outliers: 38


# Import zscore function
from scipy.stats import zscore

# Calculate z-score for each data point and compute its absolute value
z_scores = zscore(df['Age'])
abs_z_scores = np.abs(z_scores)

# Select the outliers using a threshold of 3
outliers = df[abs_z_scores > 3]
outliers.head()


# Obtain number of outliers
print(f'Number of outliers: {len(outliers)}')

Number of outliers: 22


# Import the median_abs_deviation function
from scipy.stats import median_abs_deviation

# Obtain the MAD value
mad_score = median_abs_deviation(df['Height'])
mad_score

5.079999999999984


# Calculate the percentiles
seventy_fifth = df['Height'].quantile(0.75)
twenty_fifth = df['Height'].quantile(0.25)

# Obtain IQR
iqr = seventy_fifth - twenty_fifth

# Upper and lower thresholds
upper = seventy_fifth + (1.5 * iqr)
lower = twenty_fifth - (1.5 * iqr)

# Subset the dataset
outliers = df[(df['Height'] < lower) | (df['Height'] > upper)]
outliers.head()


# Obtain number of outliers
print(f'Number of outliers: {len(outliers)}')

Number of outliers: 41


# Calculate the median
median_value = df['Height'].median()

# Impute outliers with the median
df_imputed = df.copy()
df_imputed.loc[outliers.index, 'Height'] = median_value


from scipy.stats.mstats import winsorize

df_winsorized = df.copy()
df_winsorized['Total'] = winsorize(df_winsorized['Height'],\
  limits = [0.05, 0.05], inplace = True)


# Create a box plot
g = sns.boxplot(data = df, x = 'Height')

# Add a title and change xlabel
g.set_title('Box Plot of Height')
g.set_xlabel('Height')

Text(0.5, 0, 'Height')


# Scatter plot
fig, ax = plt.subplots(figsize = (6,4))
ax.scatter(df['Height'],df['Weight'])

# x-axis label
ax.set_xlabel('(Height)')

# y-axis label
ax.set_ylabel('(Weight )')
plt.show()


# Z score
from scipy import stats
import numpy as np

z = np.abs(stats.zscore(df['Height']))
print(z)

0        1.646010
1        0.995907
2        0.891177
3        1.750741
4        0.136343
           ...   
18202    0.891177
18203    1.373324
18204    1.268593
18205    0.513760
18206    0.513760
Name: Height, Length: 18159, dtype: float64


threshold = 2

# Position of the outlier
print(np.where(z > 2))

(array([   14,    19,    29,    63,    92,   102,   109,   115,   151,
         165,   204,   210,   225,   229,   235,   261,   274,   279,
         287,   293,   303,   310,   317,   372,   380,   389,   457,
         463,   466,   467,   544,   545,   582,   634,   639,   704,
         718,   731,   733,   740,   748,   762,   767,   802,   874,
         882,   979,  1010,  1084,  1127,  1132,  1165,  1175,  1193,
        1204,  1213,  1225,  1226,  1249,  1278,  1340,  1364,  1379,
        1411,  1412,  1416,  1425,  1426,  1428,  1429,  1431,  1453,
        1454,  1461,  1475,  1493,  1498,  1550,  1558,  1564,  1581,
        1599,  1623,  1631,  1641,  1680,  1684,  1719,  1744,  1780,
        1795,  1804,  1817,  1819,  1840,  1857,  1859,  1902,  1911,
        1984,  2063,  2075,  2077,  2091,  2094,  2104,  2129,  2177,
        2187,  2203,  2216,  2231,  2262,  2289,  2301,  2329,  2335,
        2344,  2355,  2356,  2357,  2433,  2436,  2493,  2521,  2537,
        2545,  2559,  2612,  2627,  2632,  2664,  2672,  2723,  2726,
        2732,  2736,  2750,  2766,  2768,  2770,  2781,  2792,  2815,
        2818,  2828,  2834,  2859,  2895,  2957,  2977,  2984,  3060,
        3121,  3137,  3146,  3221,  3259,  3326,  3346,  3374,  3379,
        3390,  3394,  3409,  3448,  3466,  3475,  3480,  3506,  3513,
        3533,  3559,  3579,  3588,  3614,  3651,  3657,  3712,  3724,
        3764,  3769,  3801,  3837,  3878,  3886,  3890,  3910,  3979,
        3981,  4017,  4062,  4161,  4174,  4177,  4189,  4204,  4236,
        4247,  4281,  4336,  4338,  4378,  4379,  4390,  4408,  4414,
        4418,  4424,  4428,  4453,  4454,  4460,  4473,  4515,  4526,
        4529,  4542,  4606,  4653,  4712,  4726,  4777,  4782,  4800,
        4860,  4868,  4874,  4945,  4963,  4974,  5013,  5049,  5053,
        5072,  5086,  5103,  5109,  5135,  5145,  5182,  5191,  5193,
        5205,  5211,  5216,  5244,  5285,  5307,  5345,  5350,  5402,
        5422,  5428,  5431,  5441,  5443,  5469,  5479,  5488,  5491,
        5521,  5539,  5544,  5553,  5566,  5569,  5575,  5578,  5579,
        5620,  5622,  5629,  5643,  5669,  5680,  5693,  5696,  5736,
        5785,  5790,  5822,  5828,  5833,  5843,  5851,  5852,  5874,
        5884,  5921,  5935,  5958,  5963,  5983,  5986,  5987,  6008,
        6028,  6038,  6083,  6101,  6110,  6115,  6121,  6124,  6143,
        6145,  6152,  6166,  6173,  6195,  6238,  6256,  6263,  6275,
        6284,  6296,  6304,  6340,  6350,  6353,  6362,  6375,  6376,
        6394,  6424,  6430,  6441,  6455,  6475,  6488,  6535,  6569,
        6574,  6584,  6627,  6628,  6631,  6667,  6673,  6694,  6695,
        6699,  6704,  6713,  6732,  6761,  6782,  6788,  6813,  6814,
        6823,  6880,  6893,  6895,  6915,  6982,  6985,  6994,  7009,
        7012,  7025,  7117,  7126,  7141,  7156,  7174,  7181,  7279,
        7348,  7353,  7356,  7409,  7410,  7413,  7431,  7484,  7509,
        7545,  7558,  7578,  7594,  7613,  7681,  7713,  7762,  7763,
        7785,  7788,  7806,  7809,  7829,  7865,  7909,  7956,  7962,
        7968,  7998,  8005,  8040,  8059,  8117,  8119,  8148,  8151,
        8166,  8168,  8191,  8196,  8200,  8217,  8223,  8269,  8281,
        8321,  8334,  8348,  8389,  8392,  8393,  8410,  8419,  8426,
        8451,  8480,  8495,  8496,  8504,  8533,  8534,  8536,  8646,
        8667,  8674,  8690,  8706,  8727,  8735,  8740,  8766,  8772,
        8781,  8813,  8820,  8838,  8845,  8859,  8867,  8874,  8908,
        8930,  8935,  8943,  8946,  8960,  8991,  9009,  9047,  9060,
        9126,  9159,  9233,  9258,  9287,  9350,  9356,  9363,  9375,
        9384,  9427,  9441,  9458,  9485,  9491,  9510,  9521,  9559,
        9570,  9583,  9588,  9639,  9648,  9666,  9710,  9740,  9757,
        9759,  9768,  9769,  9795,  9809,  9823,  9864,  9907,  9993,
        9997, 10000, 10046, 10075, 10080, 10113, 10157, 10180, 10214,
       10227, 10239, 10249, 10251, 10282, 10348, 10376, 10387, 10426,
       10440, 10465, 10537, 10558, 10637, 10653, 10666, 10699, 10705,
       10747, 10835, 10865, 10875, 10896, 10964, 10974, 11001, 11042,
       11071, 11077, 11096, 11112, 11126, 11127, 11154, 11158, 11199,
       11226, 11277, 11318, 11320, 11340, 11345, 11375, 11376, 11403,
       11418, 11432, 11436, 11487, 11495, 11503, 11517, 11537, 11598,
       11614, 11620, 11625, 11642, 11655, 11767, 11783, 11796, 11820,
       11866, 11903, 11923, 11934, 11935, 11973, 12049, 12068, 12075,
       12124, 12158, 12163, 12169, 12186, 12257, 12262, 12266, 12308,
       12318, 12437, 12458, 12460, 12468, 12470, 12480, 12494, 12531,
       12537, 12564, 12581, 12587, 12590, 12630, 12631, 12632, 12636,
       12666, 12668, 12751, 12781, 12817, 12832, 12853, 12854, 12941,
       12951, 12959, 12967, 12975, 12987, 13063, 13080, 13087, 13109,
       13112, 13178, 13180, 13233, 13237, 13290, 13303, 13308, 13322,
       13328, 13362, 13383, 13407, 13436, 13449, 13472, 13477, 13488,
       13489, 13492, 13493, 13502, 13549, 13556, 13559, 13695, 13738,
       13745, 13747, 13754, 13762, 13806, 13890, 13905, 13917, 13942,
       13950, 14036, 14038, 14053, 14059, 14062, 14063, 14069, 14071,
       14085, 14086, 14113, 14121, 14122, 14143, 14178, 14231, 14245,
       14253, 14256, 14269, 14270, 14296, 14299, 14326, 14335, 14383,
       14388, 14389, 14411, 14414, 14421, 14432, 14463, 14494, 14496,
       14500, 14506, 14518, 14538, 14567, 14568, 14584, 14591, 14602,
       14646, 14647, 14663, 14680, 14727, 14734, 14789, 14791, 14822,
       14846, 14868, 14922, 14932, 14944, 15026, 15050, 15068, 15104,
       15115, 15133, 15175, 15177, 15181, 15192, 15195, 15221, 15228,
       15231, 15246, 15250, 15271, 15278, 15295, 15298, 15301, 15305,
       15306, 15326, 15331, 15335, 15337, 15339, 15365, 15384, 15396,
       15413, 15483, 15485, 15496, 15506, 15507, 15537, 15554, 15572,
       15576, 15588, 15603, 15616, 15637, 15639, 15671, 15716, 15726,
       15749, 15780, 15827, 15830, 15843, 15850, 15855, 15892, 15930,
       15931, 15942, 15989, 16018, 16042, 16044, 16059, 16081, 16083,
       16129, 16161, 16218, 16219, 16236, 16255, 16261, 16291, 16372,
       16401, 16407, 16413, 16428, 16435, 16455, 16483, 16493, 16498,
       16521, 16526, 16531, 16536, 16537, 16546, 16560, 16563, 16582,
       16646, 16647, 16704, 16721, 16752, 16758, 16763, 16790, 16796,
       16801, 16820, 16829, 16854, 16856, 16875, 16877, 16899, 16917,
       16930, 16954, 16990, 17008, 17042, 17051, 17052, 17068, 17069,
       17071, 17084, 17103, 17109, 17127, 17156, 17174, 17180, 17187,
       17221, 17227, 17239, 17296, 17299, 17331, 17334, 17335, 17340,
       17369, 17395, 17398, 17419, 17423, 17449, 17453, 17462, 17484,
       17521, 17542, 17546, 17555, 17571, 17595, 17609, 17646, 17698,
       17725, 17738, 17770, 17801, 17813, 17872, 17873, 17879, 17880,
       17906, 18002, 18022, 18046, 18110, 18111, 18151], dtype=int64),)


# IQR
Q1 = np.percentile(df['Height'], 25, method='midpoint')
Q3 = np.percentile(df['Height'], 75, method='midpoint')
IQR = Q3 - Q1
print(IQR)

10.159999999999997


# Above Upper bound
upper=Q3+1.5*IQR
upper_array=np.array(df['Height']>=upper)
print("Upper Bound:",upper)
print(upper_array.sum())

#Below Lower bound
lower=Q1-1.5*IQR
lower_array=np.array(df['Height']<=lower)
print("Lower Bound:",lower)
print(lower_array.sum())

Upper Bound: 200.65999999999997
33
Lower Bound: 160.01999999999998
8


import numpy as np 
import pandas as pd 

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.ensemble import IsolationForest
from sklearn.covariance import EllipticEnvelope
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler
from sklearn.svm import OneClassSVM
from sklearn.neighbors import LocalOutlierFactor


from sklearn.ensemble import IsolationForest


df['Height'].hist(figsize=(15,10));


import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN


sns.heatmap(df.corr(), annot=True)

<Axes: >


ax = sns.lmplot(x = "Height",
               y = "Weight",
               data = df, hue = "Age", fit_reg = False, height = 5, aspect = 2.2)
sns.regplot(x = "Height",
               y = "Weight",
               data = df, scatter=False, ax=ax.axes[0, 0], order = 3)
plt.ylabel("Weight")
plt.xticks(list(range(1,30)), list(df['Height'].unique()))
plt.title("Relationship Between Height and Weight", fontsize=18)
plt.xlabel("Height", fontsize=14)
plt.ylabel("Weight", fontsize=14)
plt.show()

---------------------------------------------------------------------------
UFuncTypeError                            Traceback (most recent call last)
Cell In[325], line 4
      1 ax = sns.lmplot(x = "Height",
      2                y = "Weight",
      3                data = df, hue = "Age", fit_reg = False, height = 5, aspect = 2.2)
----> 4 sns.regplot(x = "Height",
      5                y = "Weight",
      6                data = df, scatter=False, ax=ax.axes[0, 0], order = 3)
      7 plt.ylabel("Weight")
      8 plt.xticks(list(range(1,30)), list(df['Height'].unique()))

File ~\anaconda3\Lib\site-packages\seaborn\regression.py:759, in regplot(data, x, y, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)
    757 scatter_kws["marker"] = marker
    758 line_kws = {} if line_kws is None else copy.copy(line_kws)
--> 759 plotter.plot(ax, scatter_kws, line_kws)
    760 return ax

File ~\anaconda3\Lib\site-packages\seaborn\regression.py:368, in _RegressionPlotter.plot(self, ax, scatter_kws, line_kws)
    365     self.scatterplot(ax, scatter_kws)
    367 if self.fit_reg:
--> 368     self.lineplot(ax, line_kws)
    370 # Label the axes
    371 if hasattr(self.x, "name"):

File ~\anaconda3\Lib\site-packages\seaborn\regression.py:413, in _RegressionPlotter.lineplot(self, ax, kws)
    411 """Draw the model."""
    412 # Fit the regression model
--> 413 grid, yhat, err_bands = self.fit_regression(ax)
    414 edges = grid[0], grid[-1]
    416 # Get set default aesthetics

File ~\anaconda3\Lib\site-packages\seaborn\regression.py:204, in _RegressionPlotter.fit_regression(self, ax, x_range, grid)
    202 # Fit the regression
    203 if self.order > 1:
--> 204     yhat, yhat_boots = self.fit_poly(grid, self.order)
    205 elif self.logistic:
    206     from statsmodels.genmod.generalized_linear_model import GLM

File ~\anaconda3\Lib\site-packages\seaborn\regression.py:254, in _RegressionPlotter.fit_poly(self, grid, order)
    251     return np.polyval(np.polyfit(_x, _y, order), grid)
    253 x, y = self.x, self.y
--> 254 yhat = reg_func(x, y)
    255 if self.ci is None:
    256     return yhat, None

File ~\anaconda3\Lib\site-packages\seaborn\regression.py:251, in _RegressionPlotter.fit_poly.<locals>.reg_func(_x, _y)
    250 def reg_func(_x, _y):
--> 251     return np.polyval(np.polyfit(_x, _y, order), grid)

File <__array_function__ internals>:200, in polyfit(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\numpy\lib\polynomial.py:668, in polyfit(x, y, deg, rcond, full, w, cov)
    666 scale = NX.sqrt((lhs*lhs).sum(axis=0))
    667 lhs /= scale
--> 668 c, resids, rank, s = lstsq(lhs, rhs, rcond)
    669 c = (c.T/scale).T  # broadcast scale coefficients
    671 # warn on rank reduction, which indicates an ill conditioned matrix

File <__array_function__ internals>:200, in lstsq(*args, **kwargs)

File ~\anaconda3\Lib\site-packages\numpy\linalg\linalg.py:2285, in lstsq(a, b, rcond)
   2282 if n_rhs == 0:
   2283     # lapack can't handle n_rhs = 0 - so allocate the array one larger in that axis
   2284     b = zeros(b.shape[:-2] + (m, n_rhs + 1), dtype=b.dtype)
-> 2285 x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)
   2286 if m == 0:
   2287     x[...] = 0

UFuncTypeError: Cannot cast ufunc 'lstsq_n' input 1 from dtype('O') to dtype('float64') with casting rule 'same_kind'


# Find the pearson correlations matrix
corr = df.corr(method = 'pearson')
corr


# correaltions between Potential and Overall columns
c = np.corrcoef(df['Potential'],df['Overall'])
print('Correlations between Potential and Overall\n',c)

Correlations between Potential and Overall
 [[1.         0.66118012]
 [0.66118012 1.        ]]


plt.figure(figsize=(10,8), dpi =500)
sns.heatmap(corr,annot=True,fmt=".2f", linewidth=.5)
plt.show()


# correaltions between Potential and Overall columns
c = np.corrcoef(df['Age'],df['Overall'])
print('Correlations between Age and Overall\n',c)

Correlations between Age and Overall
 [[1.         0.45306932]
 [0.45306932 1.        ]]


#bestfit line
sns.lmplot(x="Age", y="Overall", data=df);


#scatterplot
import seaborn as sns
sns.scatterplot(x="Age", y="Overall", data=df);


sns.lmplot(x="Age", y="Overall", hue="Potential", data=df);


#Corrleation matrix
cormat = df.corr()
round(cormat,2)


#Correlation matrix to heat map
sns.heatmap(cormat);


import pingouin as pg
pg.corr(x=df['Potential'], y=df['Overall'])

---------------------------------------------------------------------------
FloatingPointError                        Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_4616\4157861621.py in ?()
      1 import pingouin as pg
----> 2 pg.corr(x=df['Potential'], y=df['Overall'])

~\anaconda3\Lib\site-packages\pingouin\correlation.py in ?(x, y, alternative, method, **kwargs)
    600     n = x.size
    601 
    602     # Compute correlation coefficient and two-sided p-value
    603     if method == "pearson":
--> 604         r, pval = pearsonr(x, y)
    605     elif method == "spearman":
    606         r, pval = spearmanr(x, y, **kwargs)
    607     elif method == "kendall":

~\anaconda3\Lib\site-packages\scipy\stats\_stats_py.py in ?(x, y, alternative, method)
   4856     # hypothesis is the beta distribution on (-1, 1) with a = b = n/2 - 1.
   4857     ab = n/2 - 1
   4858     dist = stats.beta(ab, ab, loc=-1, scale=2)
   4859     if alternative == 'two-sided':
-> 4860         prob = 2*dist.sf(abs(r))
   4861     elif alternative == 'less':
   4862         prob = dist.cdf(r)
   4863     elif alternative == 'greater':

~\anaconda3\Lib\site-packages\scipy\stats\_distn_infrastructure.py in ?(self, x)
    493     def sf(self, x):
--> 494         return self.dist.sf(x, *self.args, **self.kwds)

~\anaconda3\Lib\site-packages\scipy\stats\_distn_infrastructure.py in ?(self, x, *args, **kwds)
   2155         place(output, (1-cond0)+np.isnan(x), self.badvalue)
   2156         place(output, cond2, 1.0)
   2157         if np.any(cond):
   2158             goodargs = argsreduce(cond, *((x,)+args))
-> 2159             place(output, cond, self._sf(*goodargs))
   2160         if output.ndim == 0:
   2161             return output[()]
   2162         return output

~\anaconda3\Lib\site-packages\scipy\stats\_continuous_distns.py in ?(self, x, a, b)
    692     def _sf(self, x, a, b):
--> 693         return _boost._beta_sf(x, a, b)

FloatingPointError: underflow encountered in _beta_sf

	Unnamed: 0	ID	Age	Overall	Potential	Special	International Reputation	Weak Foot	Skill Moves	Jersey Number	...	Penalties	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes
count	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18159.000000	18159.000000	18159.000000	18147.000000	...	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000
mean	9103.000000	214298.338606	25.122206	66.238699	71.307299	1597.809908	1.113222	2.947299	2.361308	19.546096	...	48.548598	58.648274	47.281623	47.697836	45.661435	16.616223	16.391596	16.232061	16.388898	16.710887
std	5256.052511	29965.244204	4.669943	6.908930	6.136496	272.586016	0.394031	0.660456	0.756164	15.947765	...	15.704053	11.436133	19.904397	21.664004	21.289135	17.695349	16.906900	16.502864	17.034669	17.955119
min	0.000000	16.000000	16.000000	46.000000	48.000000	731.000000	1.000000	1.000000	1.000000	1.000000	...	5.000000	3.000000	3.000000	2.000000	3.000000	1.000000	1.000000	1.000000	1.000000	1.000000
25%	4551.500000	200315.500000	21.000000	62.000000	67.000000	1457.000000	1.000000	3.000000	2.000000	8.000000	...	39.000000	51.000000	30.000000	27.000000	24.000000	8.000000	8.000000	8.000000	8.000000	8.000000
50%	9103.000000	221759.000000	25.000000	66.000000	71.000000	1635.000000	1.000000	3.000000	2.000000	17.000000	...	49.000000	60.000000	53.000000	55.000000	52.000000	11.000000	11.000000	11.000000	11.000000	11.000000
75%	13654.500000	236529.500000	28.000000	71.000000	75.000000	1787.000000	1.000000	3.000000	3.000000	26.000000	...	60.000000	67.000000	64.000000	66.000000	64.000000	14.000000	14.000000	14.000000	14.000000	14.000000
max	18206.000000	246620.000000	45.000000	94.000000	95.000000	2346.000000	5.000000	5.000000	5.000000	99.000000	...	92.000000	96.000000	94.000000	93.000000	91.000000	90.000000	92.000000	91.000000	90.000000	94.000000

Missing Cells	76984
Missing Cells (%)	4.8%
Missing Columns	76
Missing Rows	18207
Avg Missing Cells per Column	864.99
Avg Missing Cells per Row	4.23

	Unnamed: 0	ID	Age	Overall	Potential	Special	International Reputation	Weak Foot	Skill Moves	Jersey Number	Crossing	Finishing	HeadingAccuracy	ShortPassing	Volleys	Dribbling	Curve	FKAccuracy	LongPassing	BallControl	Acceleration	SprintSpeed	Agility	Reactions	Balance	ShotPower	Jumping	Stamina	Strength	LongShots	Aggression	Interceptions	Positioning	Vision	Penalties	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Normalized_Wage
count	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000
mean	9103.000000	214298.338606	25.122206	66.238699	71.307299	1597.809908	1.112924	2.947438	2.360356	19.508046	49.734181	45.550911	52.298144	58.686712	42.909026	55.371001	47.170824	42.750151	52.711933	58.369459	64.443730	64.556324	63.336189	61.673587	63.797935	55.313835	64.917834	63.053276	65.139781	46.985775	55.721700	46.575163	49.826770	53.260120	48.420607	58.493656	47.156973	47.572088	45.541056	16.572417	16.348382	16.189268	16.345691	16.666831	9731.312133
std	5256.052511	29965.244204	4.669943	6.908930	6.136496	272.586016	0.393554	0.659591	0.755394	15.935210	18.340299	19.500064	17.356983	14.680105	17.671067	18.885426	18.370998	17.593545	15.307651	16.664584	15.271849	15.002398	15.101839	9.540921	14.493574	17.448546	12.267253	16.201430	12.979956	19.386223	17.580066	20.807859	19.670807	14.391148	15.879699	11.810375	20.025458	21.773243	21.389596	17.692536	16.905507	16.502101	17.032944	17.951898	21999.290406
min	0.000000	16.000000	16.000000	46.000000	48.000000	731.000000	1.000000	1.000000	1.000000	1.000000	5.000000	2.000000	4.000000	7.000000	4.000000	4.000000	6.000000	0.000000	9.000000	5.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	4551.500000	200315.500000	21.000000	62.000000	67.000000	1457.000000	1.000000	3.000000	2.000000	8.000000	38.000000	30.000000	45.000000	54.000000	30.000000	49.000000	34.000000	31.000000	43.000000	54.000000	57.000000	57.000000	55.000000	56.000000	56.000000	45.000000	58.000000	56.000000	58.000000	32.000000	44.000000	26.000000	38.000000	44.000000	39.000000	51.000000	30.000000	26.000000	24.000000	8.000000	8.000000	8.000000	8.000000	8.000000	1000.000000
50%	9103.000000	221759.000000	25.000000	66.000000	71.000000	1635.000000	1.000000	3.000000	2.000000	17.000000	54.000000	49.000000	56.000000	62.000000	44.000000	61.000000	48.000000	41.000000	56.000000	63.000000	67.000000	67.000000	66.000000	62.000000	66.000000	59.000000	66.000000	66.000000	66.000000	51.000000	59.000000	52.000000	55.000000	55.000000	49.000000	59.000000	53.000000	55.000000	52.000000	11.000000	11.000000	11.000000	11.000000	11.000000	3000.000000
75%	13654.500000	236529.500000	28.000000	71.000000	75.000000	1787.000000	1.000000	3.000000	3.000000	26.000000	64.000000	62.000000	64.000000	68.000000	57.000000	68.000000	62.000000	56.000000	64.000000	69.000000	75.000000	75.000000	74.000000	68.000000	74.000000	68.000000	73.000000	74.000000	74.000000	62.000000	69.000000	64.000000	64.000000	64.000000	60.000000	67.000000	64.000000	66.000000	64.000000	14.000000	14.000000	14.000000	14.000000	14.000000	9000.000000
max	18206.000000	246620.000000	45.000000	94.000000	95.000000	2346.000000	5.000000	5.000000	5.000000	99.000000	93.000000	95.000000	94.000000	93.000000	90.000000	97.000000	94.000000	94.000000	93.000000	96.000000	97.000000	96.000000	96.000000	96.000000	96.000000	95.000000	95.000000	96.000000	97.000000	94.000000	95.000000	92.000000	95.000000	94.000000	92.000000	96.000000	94.000000	93.000000	91.000000	90.000000	92.000000	91.000000	90.000000	94.000000	565000.000000

	Overall	Potential	Age
Overall	1.000000	0.601936	-0.309101
Potential	0.601936	1.000000	-0.945498
Age	-0.309101	-0.945498	1.000000

	Unnamed: 0	ID	Age	Overall	Potential	Special	International Reputation	Weak Foot	Skill Moves	Jersey Number	...	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Normalized_Wage
count	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	...	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000	18207.000000
mean	9103.000000	214298.338606	25.122206	66.238699	71.307299	1597.809908	1.112924	2.947438	2.360356	19.508046	...	58.493656	47.156973	47.572088	45.541056	16.572417	16.348382	16.189268	16.345691	16.666831	9731.312133
std	5256.052511	29965.244204	4.669943	6.908930	6.136496	272.586016	0.393554	0.659591	0.755394	15.935210	...	11.810375	20.025458	21.773243	21.389596	17.692536	16.905507	16.502101	17.032944	17.951898	21999.290406
min	0.000000	16.000000	16.000000	46.000000	48.000000	731.000000	1.000000	1.000000	1.000000	1.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	4551.500000	200315.500000	21.000000	62.000000	67.000000	1457.000000	1.000000	3.000000	2.000000	8.000000	...	51.000000	30.000000	26.000000	24.000000	8.000000	8.000000	8.000000	8.000000	8.000000	1000.000000
50%	9103.000000	221759.000000	25.000000	66.000000	71.000000	1635.000000	1.000000	3.000000	2.000000	17.000000	...	59.000000	53.000000	55.000000	52.000000	11.000000	11.000000	11.000000	11.000000	11.000000	3000.000000
75%	13654.500000	236529.500000	28.000000	71.000000	75.000000	1787.000000	1.000000	3.000000	3.000000	26.000000	...	67.000000	64.000000	66.000000	64.000000	14.000000	14.000000	14.000000	14.000000	14.000000	9000.000000
max	18206.000000	246620.000000	45.000000	94.000000	95.000000	2346.000000	5.000000	5.000000	5.000000	99.000000	...	96.000000	94.000000	93.000000	91.000000	90.000000	92.000000	91.000000	90.000000	94.000000	565000.000000

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause
0	0	158023	L. Messi	31	https://cdn.sofifa.org/players/4/19/158023.png	Argentina	https://cdn.sofifa.org/flags/52.png	94	94	FC Barcelona	...	96.0	33.0	28.0	26.0	6.0	11.0	15.0	14.0	8.0	€226.5M
1	1	20801	Cristiano Ronaldo	33	https://cdn.sofifa.org/players/4/19/20801.png	Portugal	https://cdn.sofifa.org/flags/38.png	94	94	Juventus	...	95.0	28.0	31.0	23.0	7.0	11.0	15.0	14.0	11.0	€127.1M
2	2	190871	Neymar Jr	26	https://cdn.sofifa.org/players/4/19/190871.png	Brazil	https://cdn.sofifa.org/flags/54.png	92	93	Paris Saint-Germain	...	94.0	27.0	24.0	33.0	9.0	9.0	15.0	15.0	11.0	€228.1M
3	3	193080	De Gea	27	https://cdn.sofifa.org/players/4/19/193080.png	Spain	https://cdn.sofifa.org/flags/45.png	91	93	Manchester United	...	68.0	15.0	21.0	13.0	90.0	85.0	87.0	88.0	94.0	€138.6M
4	4	192985	K. De Bruyne	27	https://cdn.sofifa.org/players/4/19/192985.png	Belgium	https://cdn.sofifa.org/flags/7.png	91	92	Manchester City	...	88.0	68.0	58.0	51.0	15.0	13.0	5.0	10.0	13.0	€196.4M

	Name	Club	Nationality	Overall	Age
4741	O. Pérez	Pachuca	Mexico	71	45
18183	K. Pilkington	Cambridge United	England	48	44
17726	T. Warner	Accrington Stanley	Trinidad & Tobago	53	44
10545	S. Narazaki	Nagoya Grampus	Japan	65	42
7225	C. Muñoz	CD Universidad de Concepción	Argentina	68	41

	Name	Club	Nationality	Overall	Age
18206	G. Nugent	Tranmere Rovers	England	46	16
17743	J. Olstad	Sarpsborg 08 FF	Norway	52	16
13293	H. Massengo	AS Monaco	France	62	16
16081	J. Italiano	Perth Glory	Australia	58	16
18166	N. Ayéva	Örebro SK	Sweden	48	16

	Name	Club	Nationality	Age	Penalties
206	M. Balotelli	OGC Nice	Italy	27	92.0
118	Fabinho	Liverpool	Brazil	24	91.0
16	H. Kane	Tottenham Hotspur	England	24	90.0
823	R. Jiménez	Wolverhampton Wanderers	Mexico	27	90.0
945	L. Baines	Everton	England	33	90.0

	Name	Club	Nationality	Overall	Age	SprintSpeed
55	L. Sané	Manchester City	Germany	86	22	96.0
25	K. Mbappé	Paris Saint-Germain	France	88	19	96.0
1968	Adama	Wolverhampton Wanderers	Spain	75	22	96.0
36	G. Bale	Real Madrid	Wales	88	28	95.0
10928	Maicon	Livorno	Brazil	65	25	95.0

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes
8605	8605	190939	S. Chhetri	33	https://cdn.sofifa.org/players/4/19/190939.png	India	https://cdn.sofifa.org/flags/159.png	67	67	No Club	...	30.0	24.0	44.0	10.0	7.0	11.0	9.0	10.0
10011	10011	223763	S. Jhingan	24	https://cdn.sofifa.org/players/4/19/223763.png	India	https://cdn.sofifa.org/flags/159.png	65	71	No Club	...	64.0	61.0	60.0	13.0	11.0	7.0	11.0	12.0
12598	12598	217225	J. Lalpekhlua	27	https://cdn.sofifa.org/players/4/19/217225.png	India	https://cdn.sofifa.org/flags/159.png	63	64	No Club	...	28.0	31.0	29.0	13.0	11.0	10.0	10.0	11.0
12811	12811	225213	G. Singh Sandhu	26	https://cdn.sofifa.org/players/4/19/225213.png	India	https://cdn.sofifa.org/flags/159.png	63	68	No Club	...	19.0	15.0	11.0	63.0	59.0	59.0	62.0	64.0
13508	13508	238205	A. Edathodika	31	https://cdn.sofifa.org/players/4/19/238205.png	India	https://cdn.sofifa.org/flags/159.png	62	62	No Club	...	67.0	62.0	68.0	14.0	15.0	14.0	11.0	7.0

	Name	Wage
0	L. Messi	€565
1	Cristiano Ronaldo	€405
2	Neymar Jr	€290
3	De Gea	€260
4	K. De Bruyne	€355

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause	Normalized_Wage
11	11	182521	T. Kroos	28	https://cdn.sofifa.org/players/4/19/182521.png	Germany	https://cdn.sofifa.org/flags/21.png	90	90	Real Madrid	...	72.0	79.0	69.0	10.0	11.0	13.0	7.0	10.0	€156.8	355000.0
18	18	192448	M. ter Stegen	26	https://cdn.sofifa.org/players/4/19/192448.png	Germany	https://cdn.sofifa.org/flags/21.png	89	92	FC Barcelona	...	25.0	13.0	10.0	87.0	85.0	88.0	85.0	90.0	€123.3	240000.0
22	22	167495	M. Neuer	32	https://cdn.sofifa.org/players/4/19/167495.png	Germany	https://cdn.sofifa.org/flags/21.png	89	89	FC Bayern München	...	17.0	10.0	11.0	90.0	86.0	91.0	87.0	87.0	€62.7	130000.0
34	34	178603	M. Hummels	29	https://cdn.sofifa.org/players/4/19/178603.png	Germany	https://cdn.sofifa.org/flags/21.png	88	88	FC Bayern München	...	88.0	90.0	88.0	15.0	6.0	10.0	5.0	6.0	€75.9	160000.0
55	55	222492	L. Sané	22	https://cdn.sofifa.org/players/4/19/222492.png	Germany	https://cdn.sofifa.org/flags/21.png	86	92	Manchester City	...	36.0	32.0	35.0	8.0	12.0	9.0	9.0	14.0	€125.1	195000.0

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause	Normalized_Wage
17786	17786	240218	A. Weidinger	21	https://cdn.sofifa.org/players/4/19/240218.png	Germany	https://cdn.sofifa.org/flags/21.png	52	60	SSV Jahn Regensburg	...	14.0	13.0	11.0	51.0	54.0	56.0	56.0	49.0	€105K	1000.0
7785	7785	236831	A. Seydel	22	https://cdn.sofifa.org/players/4/19/236831.png	Germany	https://cdn.sofifa.org/flags/21.png	67	76	Holstein Kiel	...	25.0	28.0	22.0	12.0	9.0	14.0	11.0	9.0	0	9000.0
13520	13520	239746	L. Watkowiak	22	https://cdn.sofifa.org/players/4/19/239746.png	Germany	https://cdn.sofifa.org/flags/21.png	62	68	SV Wehen Wiesbaden	...	7.0	13.0	13.0	65.0	59.0	64.0	60.0	66.0	€495K	1000.0
4542	4542	158657	T. Kessler	32	https://cdn.sofifa.org/players/4/19/158657.png	Germany	https://cdn.sofifa.org/flags/21.png	71	71	1. FC Köln	...	12.0	14.0	15.0	72.0	69.0	67.0	70.0	71.0	€2	10000.0
1426	1426	199833	L. Unnerstall	27	https://cdn.sofifa.org/players/4/19/199833.png	Germany	https://cdn.sofifa.org/flags/21.png	76	78	VVV-Venlo	...	11.0	17.0	15.0	76.0	75.0	72.0	74.0	77.0	0	12000.0

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause	Normalized_Wage
82	82	212622	J. Kimmich	23	https://cdn.sofifa.org/players/4/19/212622.png	Germany	https://cdn.sofifa.org/flags/21.png	85	88	FC Bayern München	...	75.0	81.0	79.0	8.0	15.0	7.0	15.0	15.0	€69.9	92000.0
184	184	202166	J. Draxler	24	https://cdn.sofifa.org/players/4/19/202166.png	Germany	https://cdn.sofifa.org/flags/21.png	83	86	Paris Saint-Germain	...	39.0	64.0	44.0	11.0	13.0	5.0	13.0	8.0	€62.6	91000.0
448	448	208333	E. Can	24	https://cdn.sofifa.org/players/4/19/208333.png	Germany	https://cdn.sofifa.org/flags/21.png	80	85	Juventus	...	82.0	81.0	80.0	14.0	8.0	8.0	13.0	11.0	€33.6	91000.0
5613	5613	238072	E. Löwen	21	https://cdn.sofifa.org/players/4/19/238072.png	Germany	https://cdn.sofifa.org/flags/21.png	70	80	1. FC Nürnberg	...	61.0	66.0	60.0	5.0	10.0	5.0	12.0	13.0	€5	9000.0
8607	8607	158172	M. Parensen	32	https://cdn.sofifa.org/players/4/19/158172.png	Germany	https://cdn.sofifa.org/flags/21.png	67	67	1. FC Union Berlin	...	66.0	63.0	65.0	7.0	15.0	7.0	9.0	9.0	€680K	9000.0

	Overall	Potential
Name
Vinícius Júnior	77	92
G. Donnarumma	82	93
O. Dembélé	83	92
K. Mbappé	88	95
P. Dybala	89	94
D. Godín	90	90
J. Oblak	90	93
De Gea	91	93
E. Hazard	91	91
K. De Bruyne	91	92
L. Modrić	91	91
L. Suárez	91	91
Sergio Ramos	91	91
Neymar Jr	92	93
Cristiano Ronaldo	94	94
L. Messi	94	94

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause	Normalized_Wage
16	16	202126	H. Kane	24	https://cdn.sofifa.org/players/4/19/202126.png	England	https://cdn.sofifa.org/flags/14.png	89	91	Tottenham Hotspur	...	56.0	36.0	38.0	8.0	10.0	11.0	14.0	11.0	€160.7	205000.0
60	60	202652	R. Sterling	23	https://cdn.sofifa.org/players/4/19/202652.png	England	https://cdn.sofifa.org/flags/14.png	86	89	Manchester City	...	47.0	58.0	54.0	15.0	12.0	12.0	15.0	9.0	€108.8	195000.0
117	117	211117	D. Alli	22	https://cdn.sofifa.org/players/4/19/211117.png	England	https://cdn.sofifa.org/flags/14.png	84	90	Tottenham Hotspur	...	70.0	70.0	63.0	7.0	6.0	9.0	11.0	8.0	€87.1	115000.0
135	135	188377	K. Walker	28	https://cdn.sofifa.org/players/4/19/188377.png	England	https://cdn.sofifa.org/flags/14.png	84	84	Manchester City	...	78.0	84.0	83.0	12.0	6.0	16.0	15.0	8.0	€45.3	165000.0
180	180	204935	J. Pickford	24	https://cdn.sofifa.org/players/4/19/204935.png	England	https://cdn.sofifa.org/flags/14.png	83	88	Everton	...	16.0	20.0	12.0	83.0	78.0	88.0	81.0	86.0	€49.4	78000.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18198	18198	242844	J. Livesey	18	https://cdn.sofifa.org/players/4/19/242844.png	England	https://cdn.sofifa.org/flags/14.png	47	70	Burton Albion	...	15.0	11.0	13.0	46.0	52.0	58.0	42.0	48.0	€165K	1000.0
18202	18202	238813	J. Lundstram	19	https://cdn.sofifa.org/players/4/19/238813.png	England	https://cdn.sofifa.org/flags/14.png	47	65	Crewe Alexandra	...	40.0	48.0	47.0	10.0	13.0	7.0	8.0	9.0	€143K	1000.0
18204	18204	241638	B. Worman	16	https://cdn.sofifa.org/players/4/19/241638.png	England	https://cdn.sofifa.org/flags/14.png	47	67	Cambridge United	...	32.0	13.0	11.0	6.0	5.0	10.0	6.0	13.0	€165K	1000.0
18205	18205	246268	D. Walker-Rice	17	https://cdn.sofifa.org/players/4/19/246268.png	England	https://cdn.sofifa.org/flags/14.png	47	66	Tranmere Rovers	...	20.0	25.0	27.0	14.0	6.0	14.0	8.0	9.0	€143K	1000.0
18206	18206	246269	G. Nugent	16	https://cdn.sofifa.org/players/4/19/246269.png	England	https://cdn.sofifa.org/flags/14.png	46	66	Tranmere Rovers	...	40.0	43.0	50.0	10.0	15.0	9.0	12.0	9.0	€165K	1000.0

	Acceleration	Position	Age	Nationality	SprintSpeed
Name
Douglas Costa	97.0	LM	27	Brazil	93.0
Adama	97.0	RW	22	Spain	96.0
K. Mbappé	96.0	RM	19	France	96.0
K. Manneh	96.0	LM	23	United States	93.0
S. Mané	95.0	LM	26	Senegal	93.0
R. Sterling	95.0	RW	23	England	92.0
K. Coman	95.0	LM	22	France	93.0

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause
41	41	1179	G. Buffon	40	https://cdn.sofifa.org/players/4/19/1179.png	Italy	https://cdn.sofifa.org/flags/27.png	88	88	Paris Saint-Germain	...	70.0	13.0	11.0	11.0	88.0	87.0	74.0	90.0	83.0	€7.4M
554	554	49031	S. Sorrentino	39	https://cdn.sofifa.org/players/4/19/49031.png	Italy	https://cdn.sofifa.org/flags/27.png	80	80	Chievo Verona	...	66.0	25.0	10.0	13.0	81.0	82.0	66.0	82.0	79.0	€1.7M
864	864	153260	Hilton	40	https://cdn.sofifa.org/players/4/19/153260.png	Brazil	https://cdn.sofifa.org/flags/54.png	78	78	Montpellier HSC	...	70.0	83.0	77.0	76.0	12.0	7.0	11.0	12.0	13.0	NaN
1120	1120	156092	J. Villar	41	https://cdn.sofifa.org/players/4/19/156092.png	Paraguay	https://cdn.sofifa.org/flags/58.png	77	77	NaN	...	55.0	13.0	13.0	14.0	75.0	75.0	74.0	78.0	77.0	NaN
1294	1294	14907	A. Bizzarri	40	https://cdn.sofifa.org/players/4/19/14907.png	Argentina	https://cdn.sofifa.org/flags/52.png	76	76	Foggia	...	60.0	11.0	12.0	11.0	76.0	74.0	66.0	82.0	76.0	€840K

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause
19	19	192119	T. Courtois	26	https://cdn.sofifa.org/players/4/19/192119.png	Belgium	https://cdn.sofifa.org/flags/7.png	89	90	Real Madrid	...	66.0	20.0	18.0	16.0	85.0	91.0	72.0	86.0	88.0	€113.7M
115	115	212190	N. Süle	22	https://cdn.sofifa.org/players/4/19/212190.png	Germany	https://cdn.sofifa.org/flags/21.png	84	90	FC Bayern München	...	72.0	82.0	85.0	83.0	15.0	7.0	14.0	7.0	15.0	€67.5M
165	165	213331	J. Tah	22	https://cdn.sofifa.org/players/4/19/213331.png	Germany	https://cdn.sofifa.org/flags/21.png	83	88	Bayer 04 Leverkusen	...	75.0	80.0	88.0	84.0	11.0	8.0	7.0	9.0	14.0	€52.4M
210	210	179783	R. Fährmann	29	https://cdn.sofifa.org/players/4/19/179783.png	Germany	https://cdn.sofifa.org/flags/21.png	83	84	FC Schalke 04	...	61.0	10.0	12.0	10.0	83.0	81.0	52.0	82.0	87.0	€35.5M
259	259	203263	H. Maguire	25	https://cdn.sofifa.org/players/4/19/203263.png	England	https://cdn.sofifa.org/flags/14.png	82	85	Leicester City	...	79.0	81.0	84.0	81.0	14.0	16.0	9.0	14.0	9.0	€46.4M

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause
463	463	203841	N. Pope	26	https://cdn.sofifa.org/players/4/19/203841.png	England	https://cdn.sofifa.org/flags/14.png	80	83	Burnley	...	62.0	14.0	15.0	14.0	79.0	80.0	74.0	80.0	80.0	€25.7M
1204	1204	220932	L. Kalinić	28	https://cdn.sofifa.org/players/4/19/220932.png	Croatia	https://cdn.sofifa.org/flags/10.png	76	78	KAA Gent	...	58.0	13.0	17.0	17.0	81.0	75.0	42.0	76.0	80.0	€10.2M
1340	1340	183895	M. Moralez	31	https://cdn.sofifa.org/players/4/19/183895.png	Argentina	https://cdn.sofifa.org/flags/52.png	76	76	New York City FC	...	76.0	48.0	58.0	51.0	6.0	5.0	14.0	14.0	9.0	€10.5M
1454	1454	172203	F. Forster	30	https://cdn.sofifa.org/players/4/19/172203.png	England	https://cdn.sofifa.org/flags/14.png	76	77	Southampton	...	56.0	11.0	13.0	12.0	72.0	76.0	64.0	77.0	78.0	€10.9M
2493	2493	202184	J. Plata	26	https://cdn.sofifa.org/players/4/19/202184.png	Ecuador	https://cdn.sofifa.org/flags/57.png	74	74	Real Salt Lake	...	70.0	34.0	29.0	27.0	11.0	15.0	9.0	13.0	13.0	€9M

	Unnamed: 0	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	...	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause
1132	1132	221634	L. Acosta	24	https://cdn.sofifa.org/players/4/19/221634.png	Argentina	https://cdn.sofifa.org/flags/52.png	77	79	DC United	...	74.0	45.0	47.0	31.0	13.0	9.0	6.0	12.0	10.0	€17.3M
1165	1165	170719	D. Buonanotte	30	https://cdn.sofifa.org/players/4/19/170719.png	Argentina	https://cdn.sofifa.org/flags/52.png	77	77	Universidad Católica	...	71.0	35.0	31.0	18.0	15.0	16.0	16.0	9.0	13.0	€11.9M
1340	1340	183895	M. Moralez	31	https://cdn.sofifa.org/players/4/19/183895.png	Argentina	https://cdn.sofifa.org/flags/52.png	76	76	New York City FC	...	76.0	48.0	58.0	51.0	6.0	5.0	14.0	14.0	9.0	€10.5M
1680	1680	214327	V. Hernández	29	https://cdn.sofifa.org/players/4/19/214327.png	Colombia	https://cdn.sofifa.org/flags/56.png	75	75	Atlético Nacional	...	77.0	62.0	20.0	21.0	10.0	15.0	15.0	13.0	11.0	€9.1M
2493	2493	202184	J. Plata	26	https://cdn.sofifa.org/players/4/19/202184.png	Ecuador	https://cdn.sofifa.org/flags/57.png	74	74	Real Salt Lake	...	70.0	34.0	29.0	27.0	11.0	15.0	9.0	13.0	13.0	€9M

	Unnamed: 0	ID	Age	Overall	Potential	Special	International Reputation	Weak Foot	Skill Moves	Jersey Number	...	Penalties	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes
Unnamed: 0	1.000000	0.416108	-0.455707	-0.972796	-0.633444	-0.596913	-0.413322	-0.204026	-0.415672	0.213645	...	-0.337899	-0.716173	-0.280829	-0.248564	-0.220034	0.027757	0.027026	0.030654	0.019842	0.025610
ID	0.416108	1.000000	-0.739162	-0.417354	0.046577	-0.231366	-0.356191	-0.075784	-0.056914	0.182074	...	-0.140657	-0.384473	-0.110198	-0.085929	-0.068409	-0.105594	-0.111149	-0.106652	-0.118250	-0.105778
Age	-0.455707	-0.739162	1.000000	0.453069	-0.252281	0.236875	0.253765	0.059867	0.027649	-0.241156	...	0.139535	0.391023	0.142817	0.119745	0.103089	0.101277	0.106419	0.104964	0.116402	0.103313
Overall	-0.972796	-0.417354	0.453069	1.000000	0.661180	0.607236	0.499491	0.212015	0.414463	-0.218931	...	0.341429	0.727655	0.286505	0.252629	0.222811	-0.025937	-0.025062	-0.029372	-0.017674	-0.023276
Potential	-0.633444	0.046577	-0.252281	0.661180	1.000000	0.384598	0.372993	0.162346	0.354290	-0.010474	...	0.224281	0.440008	0.162801	0.143564	0.128980	-0.053446	-0.054672	-0.059061	-0.052589	-0.053341
Special	-0.596913	-0.231366	0.236875	0.607236	0.384598	1.000000	0.292208	0.341855	0.763412	-0.133716	...	0.734533	0.752331	0.561866	0.538802	0.506968	-0.674637	-0.673625	-0.670254	-0.668272	-0.673238
International Reputation	-0.413322	-0.356191	0.253765	0.499491	0.372993	0.292208	1.000000	0.128317	0.208153	-0.077298	...	0.218620	0.392787	0.115208	0.092846	0.079176	0.004526	0.003942	0.000651	0.006904	0.003444
Weak Foot	-0.204026	-0.075784	0.059867	0.212015	0.162346	0.341855	0.128317	1.000000	0.340721	-0.035410	...	0.330252	0.278132	0.065673	0.042646	0.026105	-0.231905	-0.233098	-0.229395	-0.231298	-0.232574
Skill Moves	-0.415672	-0.056914	0.027649	0.414463	0.354290	0.763412	0.208153	0.340721	1.000000	-0.035194	...	0.690434	0.586836	0.241428	0.210517	0.178607	-0.621675	-0.619755	-0.616990	-0.618853	-0.621925
Jersey Number	0.213645	0.182074	-0.241156	-0.218931	-0.010474	-0.133716	-0.077298	-0.035410	-0.035194	1.000000	...	-0.028023	-0.167523	-0.142474	-0.133285	-0.124610	0.004807	0.001543	0.001162	-0.002736	0.003255
Height	-0.034734	-0.090199	0.082604	0.038546	-0.009807	-0.382862	0.034881	-0.174793	-0.422753	-0.039469	...	-0.340664	-0.135785	-0.073733	-0.058877	-0.066869	0.360594	0.360796	0.358780	0.361916	0.362636
Weight	-0.150844	-0.191425	0.230213	0.154634	-0.006947	-0.267830	0.088340	-0.130724	-0.351209	-0.087319	...	-0.253387	-0.034444	-0.049356	-0.046835	-0.056164	0.340034	0.339024	0.337717	0.342178	0.341135
Crossing	-0.390062	-0.131994	0.130545	0.394972	0.246319	0.866417	0.191770	0.307925	0.741035	-0.076585	...	0.645805	0.575446	0.443101	0.428963	0.409961	-0.663053	-0.660193	-0.659767	-0.660160	-0.662539
Finishing	-0.325529	-0.082323	0.068660	0.332515	0.243355	0.724244	0.178373	0.357416	0.743439	-0.006639	...	0.837827	0.533414	0.024218	-0.033023	-0.071811	-0.588752	-0.587145	-0.583268	-0.584852	-0.586913
HeadingAccuracy	-0.337766	-0.106815	0.147183	0.340776	0.200988	0.644421	0.157483	0.183238	0.443005	-0.091688	...	0.551978	0.507208	0.583123	0.561063	0.533643	-0.750417	-0.749888	-0.746444	-0.744443	-0.748895
ShortPassing	-0.492495	-0.136279	0.132894	0.502550	0.369189	0.906729	0.242803	0.322133	0.730363	-0.100241	...	0.676063	0.685137	0.559576	0.541131	0.508644	-0.729785	-0.728024	-0.724381	-0.723782	-0.728721
Volleys	-0.384285	-0.159915	0.142472	0.391338	0.254906	0.773974	0.243089	0.357340	0.745077	-0.026731	...	0.829257	0.595281	0.120919	0.072788	0.035457	-0.590808	-0.588668	-0.584954	-0.586131	-0.588670
Dribbling	-0.364106	-0.030340	0.010166	0.372426	0.315019	0.874274	0.179041	0.352658	0.839757	-0.028021	...	0.769594	0.597498	0.336072	0.301251	0.273963	-0.754625	-0.753181	-0.749816	-0.751348	-0.754341
Curve	-0.416378	-0.169511	0.143276	0.419491	0.279944	0.851900	0.233681	0.345468	0.771052	-0.055428	...	0.751833	0.616532	0.289529	0.261481	0.232869	-0.606286	-0.603141	-0.600266	-0.603540	-0.604960
FKAccuracy	-0.395433	-0.199549	0.193467	0.396892	0.230544	0.806414	0.223564	0.330472	0.701068	-0.068843	...	0.734440	0.585120	0.297976	0.279153	0.247903	-0.556605	-0.553644	-0.549911	-0.552641	-0.554920
LongPassing	-0.477500	-0.186764	0.181310	0.483909	0.321437	0.846302	0.239525	0.277174	0.622342	-0.117424	...	0.542247	0.645797	0.587106	0.587430	0.562230	-0.596820	-0.594999	-0.591453	-0.591561	-0.595887
BallControl	-0.449655	-0.100184	0.084969	0.460197	0.354396	0.912107	0.217946	0.356383	0.818051	-0.073210	...	0.769791	0.674881	0.452705	0.417566	0.384802	-0.788444	-0.786797	-0.783423	-0.783607	-0.787939
Acceleration	-0.185030	0.133236	-0.158667	0.196869	0.234608	0.654337	0.044319	0.261435	0.652356	-0.004395	...	0.532908	0.347427	0.195369	0.163000	0.157565	-0.593008	-0.594866	-0.592127	-0.592143	-0.593201
SprintSpeed	-0.198797	0.132437	-0.151682	0.210647	0.236771	0.645963	0.044070	0.248822	0.624098	-0.015069	...	0.521071	0.351607	0.212575	0.178214	0.171980	-0.597677	-0.599694	-0.597320	-0.596498	-0.597837
Agility	-0.256270	-0.019897	-0.019395	0.264952	0.222310	0.699673	0.100869	0.302062	0.681765	-0.034158	...	0.566175	0.432511	0.167122	0.129204	0.116686	-0.527756	-0.528482	-0.527164	-0.526983	-0.528899
Reactions	-0.832156	-0.408617	0.453124	0.850045	0.513425	0.597169	0.445614	0.201341	0.377044	-0.192622	...	0.346143	0.685558	0.283607	0.255399	0.228355	-0.062967	-0.061940	-0.065927	-0.055031	-0.059961
Balance	-0.097160	0.048463	-0.089877	0.103160	0.138025	0.586788	0.050076	0.254022	0.578459	0.008009	...	0.482794	0.310763	0.178695	0.154045	0.152470	-0.504727	-0.506102	-0.503970	-0.503652	-0.505974
ShotPower	-0.440023	-0.166133	0.156947	0.441118	0.288318	0.835277	0.227772	0.332855	0.718237	-0.053860	...	0.795220	0.634495	0.296944	0.256403	0.220237	-0.654117	-0.654099	-0.649403	-0.651409	-0.653475
Jumping	-0.261581	-0.169369	0.177167	0.264435	0.109151	0.321846	0.120931	0.069752	0.107553	-0.104179	...	0.133294	0.252353	0.279196	0.260645	0.260261	-0.192700	-0.193692	-0.195282	-0.189079	-0.192050
Stamina	-0.358451	-0.053895	0.097793	0.365656	0.202563	0.792762	0.094780	0.232094	0.570226	-0.127822	...	0.516426	0.523112	0.587782	0.570055	0.544702	-0.701467	-0.698556	-0.696729	-0.696073	-0.699670
Strength	-0.342839	-0.259756	0.332798	0.349326	0.075769	0.192990	0.131280	-0.008470	-0.041475	-0.158411	...	0.054491	0.280522	0.333334	0.332159	0.304849	-0.111012	-0.109660	-0.110253	-0.103878	-0.107497
LongShots	-0.417853	-0.161549	0.155096	0.420795	0.266740	0.840049	0.213960	0.355967	0.752980	-0.046174	...	0.812446	0.616102	0.215510	0.172331	0.133603	-0.612381	-0.610739	-0.605952	-0.607200	-0.610087
Aggression	-0.397067	-0.228329	0.265190	0.395470	0.171174	0.666236	0.173327	0.131524	0.347795	-0.146907	...	0.336089	0.515776	0.723961	0.744216	0.721384	-0.575843	-0.576114	-0.573607	-0.571201	-0.575142
Interceptions	-0.319162	-0.160602	0.197845	0.321326	0.154908	0.561676	0.129586	0.053097	0.209604	-0.158526	...	0.110834	0.397450	0.888349	0.941471	0.928282	-0.485585	-0.486324	-0.485394	-0.481279	-0.486036
Positioning	-0.351820	-0.088330	0.082443	0.356493	0.245616	0.824307	0.183003	0.346896	0.781248	-0.025422	...	0.801268	0.580498	0.202597	0.158060	0.124228	-0.679480	-0.677699	-0.674393	-0.675569	-0.678582
Vision	-0.490296	-0.215170	0.187422	0.498894	0.348141	0.761992	0.284600	0.337897	0.674057	-0.078050	...	0.632927	0.636280	0.176760	0.146460	0.113228	-0.381899	-0.377807	-0.374737	-0.375775	-0.381158
Penalties	-0.337899	-0.140657	0.139535	0.341429	0.224281	0.734533	0.218620	0.330252	0.690434	-0.028023	...	1.000000	0.551801	0.152296	0.101920	0.066693	-0.620069	-0.618968	-0.614006	-0.617074	-0.619099
Composure	-0.716173	-0.384473	0.391023	0.727655	0.440008	0.752331	0.392787	0.278132	0.586836	-0.167523	...	0.551801	1.000000	0.384081	0.351726	0.317492	-0.378750	-0.375720	-0.374897	-0.370234	-0.377626
Marking	-0.280829	-0.110198	0.142817	0.286505	0.162801	0.561866	0.115208	0.065673	0.241428	-0.142474	...	0.152296	0.384081	1.000000	0.906541	0.895908	-0.550978	-0.552263	-0.549498	-0.546670	-0.551290
StandingTackle	-0.248564	-0.085929	0.119745	0.252629	0.143564	0.538802	0.092846	0.042646	0.210517	-0.133285	...	0.101920	0.351726	0.906541	1.000000	0.974659	-0.530989	-0.532160	-0.531092	-0.527792	-0.531474
SlidingTackle	-0.220034	-0.068409	0.103089	0.222811	0.128980	0.506968	0.079176	0.026105	0.178607	-0.124610	...	0.066693	0.317492	0.895908	0.974659	1.000000	-0.509337	-0.510591	-0.509378	-0.505792	-0.509425
GKDiving	0.027757	-0.105594	0.101277	-0.025937	-0.053446	-0.674637	0.004526	-0.231905	-0.621675	0.004807	...	-0.620069	-0.378750	-0.550978	-0.530989	-0.509337	1.000000	0.970280	0.965685	0.969864	0.973320
GKHandling	0.027026	-0.111149	0.106419	-0.025062	-0.054672	-0.673625	0.003942	-0.233098	-0.619755	0.001543	...	-0.618968	-0.375720	-0.552263	-0.532160	-0.510591	0.970280	1.000000	0.965239	0.969408	0.970264
GKKicking	0.030654	-0.106652	0.104964	-0.029372	-0.059061	-0.670254	0.000651	-0.229395	-0.616990	0.001162	...	-0.614006	-0.374897	-0.549498	-0.531092	-0.509378	0.965685	0.965239	1.000000	0.964336	0.966337
GKPositioning	0.019842	-0.118250	0.116402	-0.017674	-0.052589	-0.668272	0.006904	-0.231298	-0.618853	-0.002736	...	-0.617074	-0.370234	-0.546670	-0.527792	-0.505792	0.969864	0.969408	0.964336	1.000000	0.970130
GKReflexes	0.025610	-0.105778	0.103313	-0.023276	-0.053341	-0.673238	0.003444	-0.232574	-0.621925	0.003255	...	-0.619099	-0.377626	-0.551290	-0.531474	-0.509425	0.973320	0.970264	0.966337	0.970130	1.000000

	Unnamed: 0	ID	Age	Overall	Potential	Special	International Reputation	Weak Foot	Skill Moves	Jersey Number	...	Penalties	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes
Unnamed: 0	1.00	0.42	-0.46	-0.97	-0.63	-0.60	-0.41	-0.20	-0.42	0.21	...	-0.34	-0.72	-0.28	-0.25	-0.22	0.03	0.03	0.03	0.02	0.03
ID	0.42	1.00	-0.74	-0.42	0.05	-0.23	-0.36	-0.08	-0.06	0.18	...	-0.14	-0.38	-0.11	-0.09	-0.07	-0.11	-0.11	-0.11	-0.12	-0.11
Age	-0.46	-0.74	1.00	0.45	-0.25	0.24	0.25	0.06	0.03	-0.24	...	0.14	0.39	0.14	0.12	0.10	0.10	0.11	0.10	0.12	0.10
Overall	-0.97	-0.42	0.45	1.00	0.66	0.61	0.50	0.21	0.41	-0.22	...	0.34	0.73	0.29	0.25	0.22	-0.03	-0.03	-0.03	-0.02	-0.02
Potential	-0.63	0.05	-0.25	0.66	1.00	0.38	0.37	0.16	0.35	-0.01	...	0.22	0.44	0.16	0.14	0.13	-0.05	-0.05	-0.06	-0.05	-0.05
Special	-0.60	-0.23	0.24	0.61	0.38	1.00	0.29	0.34	0.76	-0.13	...	0.73	0.75	0.56	0.54	0.51	-0.67	-0.67	-0.67	-0.67	-0.67
International Reputation	-0.41	-0.36	0.25	0.50	0.37	0.29	1.00	0.13	0.21	-0.08	...	0.22	0.39	0.12	0.09	0.08	0.00	0.00	0.00	0.01	0.00
Weak Foot	-0.20	-0.08	0.06	0.21	0.16	0.34	0.13	1.00	0.34	-0.04	...	0.33	0.28	0.07	0.04	0.03	-0.23	-0.23	-0.23	-0.23	-0.23
Skill Moves	-0.42	-0.06	0.03	0.41	0.35	0.76	0.21	0.34	1.00	-0.04	...	0.69	0.59	0.24	0.21	0.18	-0.62	-0.62	-0.62	-0.62	-0.62
Jersey Number	0.21	0.18	-0.24	-0.22	-0.01	-0.13	-0.08	-0.04	-0.04	1.00	...	-0.03	-0.17	-0.14	-0.13	-0.12	0.00	0.00	0.00	-0.00	0.00
Height	-0.03	-0.09	0.08	0.04	-0.01	-0.38	0.03	-0.17	-0.42	-0.04	...	-0.34	-0.14	-0.07	-0.06	-0.07	0.36	0.36	0.36	0.36	0.36
Weight	-0.15	-0.19	0.23	0.15	-0.01	-0.27	0.09	-0.13	-0.35	-0.09	...	-0.25	-0.03	-0.05	-0.05	-0.06	0.34	0.34	0.34	0.34	0.34
Crossing	-0.39	-0.13	0.13	0.39	0.25	0.87	0.19	0.31	0.74	-0.08	...	0.65	0.58	0.44	0.43	0.41	-0.66	-0.66	-0.66	-0.66	-0.66
Finishing	-0.33	-0.08	0.07	0.33	0.24	0.72	0.18	0.36	0.74	-0.01	...	0.84	0.53	0.02	-0.03	-0.07	-0.59	-0.59	-0.58	-0.58	-0.59
HeadingAccuracy	-0.34	-0.11	0.15	0.34	0.20	0.64	0.16	0.18	0.44	-0.09	...	0.55	0.51	0.58	0.56	0.53	-0.75	-0.75	-0.75	-0.74	-0.75
ShortPassing	-0.49	-0.14	0.13	0.50	0.37	0.91	0.24	0.32	0.73	-0.10	...	0.68	0.69	0.56	0.54	0.51	-0.73	-0.73	-0.72	-0.72	-0.73
Volleys	-0.38	-0.16	0.14	0.39	0.25	0.77	0.24	0.36	0.75	-0.03	...	0.83	0.60	0.12	0.07	0.04	-0.59	-0.59	-0.58	-0.59	-0.59
Dribbling	-0.36	-0.03	0.01	0.37	0.32	0.87	0.18	0.35	0.84	-0.03	...	0.77	0.60	0.34	0.30	0.27	-0.75	-0.75	-0.75	-0.75	-0.75
Curve	-0.42	-0.17	0.14	0.42	0.28	0.85	0.23	0.35	0.77	-0.06	...	0.75	0.62	0.29	0.26	0.23	-0.61	-0.60	-0.60	-0.60	-0.60
FKAccuracy	-0.40	-0.20	0.19	0.40	0.23	0.81	0.22	0.33	0.70	-0.07	...	0.73	0.59	0.30	0.28	0.25	-0.56	-0.55	-0.55	-0.55	-0.55
LongPassing	-0.48	-0.19	0.18	0.48	0.32	0.85	0.24	0.28	0.62	-0.12	...	0.54	0.65	0.59	0.59	0.56	-0.60	-0.59	-0.59	-0.59	-0.60
BallControl	-0.45	-0.10	0.08	0.46	0.35	0.91	0.22	0.36	0.82	-0.07	...	0.77	0.67	0.45	0.42	0.38	-0.79	-0.79	-0.78	-0.78	-0.79
Acceleration	-0.19	0.13	-0.16	0.20	0.23	0.65	0.04	0.26	0.65	-0.00	...	0.53	0.35	0.20	0.16	0.16	-0.59	-0.59	-0.59	-0.59	-0.59
SprintSpeed	-0.20	0.13	-0.15	0.21	0.24	0.65	0.04	0.25	0.62	-0.02	...	0.52	0.35	0.21	0.18	0.17	-0.60	-0.60	-0.60	-0.60	-0.60
Agility	-0.26	-0.02	-0.02	0.26	0.22	0.70	0.10	0.30	0.68	-0.03	...	0.57	0.43	0.17	0.13	0.12	-0.53	-0.53	-0.53	-0.53	-0.53
Reactions	-0.83	-0.41	0.45	0.85	0.51	0.60	0.45	0.20	0.38	-0.19	...	0.35	0.69	0.28	0.26	0.23	-0.06	-0.06	-0.07	-0.06	-0.06
Balance	-0.10	0.05	-0.09	0.10	0.14	0.59	0.05	0.25	0.58	0.01	...	0.48	0.31	0.18	0.15	0.15	-0.50	-0.51	-0.50	-0.50	-0.51
ShotPower	-0.44	-0.17	0.16	0.44	0.29	0.84	0.23	0.33	0.72	-0.05	...	0.80	0.63	0.30	0.26	0.22	-0.65	-0.65	-0.65	-0.65	-0.65
Jumping	-0.26	-0.17	0.18	0.26	0.11	0.32	0.12	0.07	0.11	-0.10	...	0.13	0.25	0.28	0.26	0.26	-0.19	-0.19	-0.20	-0.19	-0.19
Stamina	-0.36	-0.05	0.10	0.37	0.20	0.79	0.09	0.23	0.57	-0.13	...	0.52	0.52	0.59	0.57	0.54	-0.70	-0.70	-0.70	-0.70	-0.70
Strength	-0.34	-0.26	0.33	0.35	0.08	0.19	0.13	-0.01	-0.04	-0.16	...	0.05	0.28	0.33	0.33	0.30	-0.11	-0.11	-0.11	-0.10	-0.11
LongShots	-0.42	-0.16	0.16	0.42	0.27	0.84	0.21	0.36	0.75	-0.05	...	0.81	0.62	0.22	0.17	0.13	-0.61	-0.61	-0.61	-0.61	-0.61
Aggression	-0.40	-0.23	0.27	0.40	0.17	0.67	0.17	0.13	0.35	-0.15	...	0.34	0.52	0.72	0.74	0.72	-0.58	-0.58	-0.57	-0.57	-0.58
Interceptions	-0.32	-0.16	0.20	0.32	0.15	0.56	0.13	0.05	0.21	-0.16	...	0.11	0.40	0.89	0.94	0.93	-0.49	-0.49	-0.49	-0.48	-0.49
Positioning	-0.35	-0.09	0.08	0.36	0.25	0.82	0.18	0.35	0.78	-0.03	...	0.80	0.58	0.20	0.16	0.12	-0.68	-0.68	-0.67	-0.68	-0.68
Vision	-0.49	-0.22	0.19	0.50	0.35	0.76	0.28	0.34	0.67	-0.08	...	0.63	0.64	0.18	0.15	0.11	-0.38	-0.38	-0.37	-0.38	-0.38
Penalties	-0.34	-0.14	0.14	0.34	0.22	0.73	0.22	0.33	0.69	-0.03	...	1.00	0.55	0.15	0.10	0.07	-0.62	-0.62	-0.61	-0.62	-0.62
Composure	-0.72	-0.38	0.39	0.73	0.44	0.75	0.39	0.28	0.59	-0.17	...	0.55	1.00	0.38	0.35	0.32	-0.38	-0.38	-0.37	-0.37	-0.38
Marking	-0.28	-0.11	0.14	0.29	0.16	0.56	0.12	0.07	0.24	-0.14	...	0.15	0.38	1.00	0.91	0.90	-0.55	-0.55	-0.55	-0.55	-0.55
StandingTackle	-0.25	-0.09	0.12	0.25	0.14	0.54	0.09	0.04	0.21	-0.13	...	0.10	0.35	0.91	1.00	0.97	-0.53	-0.53	-0.53	-0.53	-0.53
SlidingTackle	-0.22	-0.07	0.10	0.22	0.13	0.51	0.08	0.03	0.18	-0.12	...	0.07	0.32	0.90	0.97	1.00	-0.51	-0.51	-0.51	-0.51	-0.51
GKDiving	0.03	-0.11	0.10	-0.03	-0.05	-0.67	0.00	-0.23	-0.62	0.00	...	-0.62	-0.38	-0.55	-0.53	-0.51	1.00	0.97	0.97	0.97	0.97
GKHandling	0.03	-0.11	0.11	-0.03	-0.05	-0.67	0.00	-0.23	-0.62	0.00	...	-0.62	-0.38	-0.55	-0.53	-0.51	0.97	1.00	0.97	0.97	0.97
GKKicking	0.03	-0.11	0.10	-0.03	-0.06	-0.67	0.00	-0.23	-0.62	0.00	...	-0.61	-0.37	-0.55	-0.53	-0.51	0.97	0.97	1.00	0.96	0.97
GKPositioning	0.02	-0.12	0.12	-0.02	-0.05	-0.67	0.01	-0.23	-0.62	-0.00	...	-0.62	-0.37	-0.55	-0.53	-0.51	0.97	0.97	0.96	1.00	0.97
GKReflexes	0.03	-0.11	0.10	-0.02	-0.05	-0.67	0.00	-0.23	-0.62	0.00	...	-0.62	-0.38	-0.55	-0.53	-0.51	0.97	0.97	0.97	0.97	1.00

Missing Statistics